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    1. eLife Assessment

      This study provides useful data on oscillatory coupling during speech planning, particularly suggesting that theta-band phase coupling may differentiate networks involved in object recognition and semantic-lexical retrieval. However, the strength of the evidence is incomplete because the absence of a linguistic manipulation and key confounds between the naming and control conditions limit the ability to attribute the reported effects specifically to word production, leaving the main mechanistic conclusions only partially supported.

    2. Reviewer #1 (Public review):

      Summary:

      Akkad et al. present an MEG study of overt picture naming, designed to identify oscillatory mechanisms underlying spoken language production. Participants named line drawings of common objects or responded "no" to scrambled versions of the same images. The authors focus on the period preceding articulation and report on an increase in theta power associated with picture naming in lingual cortex and left medial prefrontal cortex. Two networks are then identified: a left lingual-seeded network involving right ventral occipito-temporal cortex, interpreted as supporting object recognition. Second, a left medial prefrontal-seeded network involving medial temporal regions (and centres in the brain) supporting semantic-lexical retrieval streams. The authors further report increased theta-gamma phase-amplitude coupling in left fusiform and left inferior frontal regions, with stronger left fusiform PAC associated with slower naming responses.

      Strengths:

      The paper provides useful insight into the network-level and temporal dynamics associated with picture naming.

      Weaknesses:

      The results are somewhat over-interpreted, and the paper would benefit from a broader and more cautious discussion. The main concerns relate to the control condition and the interpretation of the theta-band effects. First, it is unclear whether the control condition adequately isolates the spoken-language network. Second, the reported theta-band effect appears broadband, raising the possibility that it reflects slow evoked responses rather than genuine oscillatory activity.

    3. Reviewer #2 (Public review):

      Summary:

      In this MEG study, the authors assess the oscillatory dynamics of object naming in 27 participants. The authors observed two distinct theta-coupled networks: a ventral occipito-temporal network linked to object recognition and a medial fronto-temporal network linked to lexico-semantic retrieval. Moreover, when contrasting object naming to a scrambled image control task, they observed increased theta-gamma phase amplitude coupling in the LIFG and fusiform gyrus. The authors conclude that theta-coupling ensures the binding of recognition and retrieval processes in object naming, while the theta-gamma phase amplitude coupling ensures local computations within those networks.

      Strengths:

      Addressing the question 'which oscillatory dynamics govern speech planning' is interesting and timely, since not much empirical evidence on this topic is currently available. Whole-brain MEG assessing phase and phase-amplitude coupling for object naming are not commonly attempted, and the study boosts a lot of power. Furthermore, the writing is concise and clear, making the paper easy to follow and pleasant to read.

      Weaknesses:

      My overall assessment is that the claims and conclusions of this study are stronger than the evidence supports, and this is because of several design weaknesses which I will detail below. Moreover, this study gives too one-sided a perspective on the current state-of-the-art in the neurophysiology of speech planning/object naming, and the presented conclusions can solely be understood from that narrow theoretical perspective.

      (1) One of my bigger concerns is the lack of any language-related manipulations that allows to independently isolate the processing components the authors discuss. Object naming (speech planning) is a complex, multifaceted mental operation where different representations and processing levels can overlap in time and space (e.g., Indefrey, 2011; Hickok, 2012; Dell et al., 2013; Munding et al., 2016; Strijkers & Costa, 2016; Nozari & Pinet, 2020; De Zubicaray, 2023; Pickering & Strijkers, 2025). Without attempting to isolate those specific representations and/or processes and solely looking at object naming in general, the only manner to interpret the data is by relying on a priori defined and often one-to-one mappings of space and function in light of a specific theoretical perspective. This is indeed what the authors do in the current study, by linking certain brain regions they observe (e.g., more occipital for object recognition and more fronto-temporal for lexico-semantics) to the function assigned in dual-route models (note also that the authors rely on the dual-route models of comprehension, not production per se; see below). While doing so is of course legitimate, it does strongly reduce the impact of the results since they do not concern a direct empirical demonstration of the authors' conclusions, but an indirect inference based on a single theoretical framework. To give a few examples of how this weakens the conclusions: How do the authors know the fronto-temporal network is lexico-semantic and not phonological, as many other models would pose (e.g., Indefrey & Level, 2004; 2011; Hickok, 2012; 2014; De Zubicaray, 2023)? Similarly, theta-coupling in production is not uniquely related to lexico-semantics, but could also be linked to the syllabic rate (speech rate; e.g., Giraud & Poeppel, 2012; Meyer, 2018), or to working memory in production (e.g., Piai & Zheng, 2019). The occipito-temporal network may be involved in object recognition, but it may also reflect (embodied) word semantics, in particular for imageable objects as in the current study (e.g., Strijkers & Costa, 2016; Pulvermuller, 2018). In my opinion, given the lack of independent manipulations, these (and other) alternative conclusions are equally plausible, and the authors' interpretations of the data pattern are tentative at best; this is a substantial limitation in my view.

      (2) The authors may object that they do have a contrast, namely the control condition. However, the control condition and the object naming condition differ in so many crucial aspects that it does not serve as a reliable control to address the above issues with interpretational strength. The control condition solely deals with low-level visual input, but not with conceptual and language-related processing, nor with motor preparation and articulation (since repeatedly saying 'no' is a flawed control for the more complex and variable naming responses required in object naming).

      (3) Moreover, the timings between the control and object naming conditions are confounded: Naming and control RTs differ by 270 ms. The 1 s pre-response window therefore spans 0.08 to 1.08 s post-stimulus in naming, but −0.19 to +0.81 s in control. Hence, part of the contrast may rather reflect 'when' something was measured rather than 'what' processing differences it is supposed to reflect.

      (4) As far as I can tell, the authors did not assess potential pre-articulation motor confounds in the data via ICA and/or recorded EMG activity (e.g., Vos et al., 2010; Porcaro et al., 2015; Strijkers et al., 2017; Abbasi et al., 2021). This may be relevant since motor preparatory activity is known to emerge well before actual articulation (e.g., Ries et al., 2013), and may (pre-)track articulatory speech rate in the theta range (e.g., Abbasi et al., 2023).

      (5) Why are items not modelled as random intercepts/slopes? With 200 different stimuli, item variability seems relevant to include in the statistical models.

      (6) I found that the authors gave a very restricted and not up-to-date literature overview on the neural basis of object naming. They mainly consider a dual route architecture. I understand that the authors don't wish (nor have to) give a complete overview of the different neurobiological models of object naming. However, the dual route model they cite is the speech processing and comprehension model (e.g., Hickok & Poeppel, 2004; 2007), which has no clear link to object naming, and they don't mention the production versions of a dual route architecture (e.g., Ueno et al., 2011; Hickok, 2012). Moreover, as mentioned also in comment 1, several other critical models (and more relevant for object naming specifically) are neglected: classical sequential object naming models (e.g., Indefrey & Levelt, 2004; Indefrey, 2011; Roelofs, 2014), interactive models (e.g., Dell et al., 2013; Dell & Chang, 2014), or more recent parallel models (e.g., Strijkers & Costa, 2016; Pickering & Costa, 2025) (for recent reviews see: e.g., Nozari & Pinet, 2020; Kerr et al., 2023; de Zubicaray, 2023). If the authors prefer to solely focus on a single class of models, that's fine (though a model that is more tightly linked to object naming would have been more relevant), but that limited scope should at the very least be acknowledged.

    4. Author response:

      We thank the editors and reviewers for their thoughtful and constructive assessment of our manuscript. We appreciate their recognition of the value of investigating oscillatory dynamics during overt picture naming and welcome the opportunity to strengthen the analyses and clarify the interpretation of our findings.

      Functional interpretation and experimental design

      We acknowledge that the contrast between picture naming and scrambled-image rejection does not independently isolate the component processes involved in spoken language production. Although the observed patterns of theta phase coupling and theta–gamma phase-amplitude coupling are consistent with the proposed involvement of visual, semantic and language-related networks, the study was designed to investigate distributed neural dynamics during overt picture naming, rather than to establish a one-to-one correspondence between these coupling patterns and specific psycholinguistic operations.

      We will therefore revise the manuscript to distinguish more clearly between the observed neurophysiological findings and their proposed functional interpretations. We will also broaden the theoretical framework to incorporate alternative accounts of the identified networks and relevant contemporary models of language production, and reconsider claims concerning the right fusiform cortex as an integration hub.

      Temporal alignment and motor-related confounds

      We recognise the reviewers' concerns regarding differences in response latency and articulatory demands between the naming and control conditions. Our analyses were response-locked to capture neural dynamics preceding articulation while accommodating variability in naming latency across trials and participants. We consider this alignment important for investigating language production processes, whose timing relative to stimulus onset may vary considerably.

      Nevertheless, we acknowledge that differences in response latency may result in the two conditions sampling different periods relative to stimulus presentation. We will examine the potential influence of this temporal difference through complementary analyses and clarify the rationale and limitations of the response-locked approach. We will also consider the potential contribution of differences in articulatory preparation between conditions when interpreting the findings.

      Interpretation of low-frequency activity

      We agree that the observed low-frequency effects warrant consideration of an alternative explanation involving transient evoked activity rather than sustained theta oscillations. We will revisit the interpretation of these findings in light of the potential contribution of transient evoked activity, drawing on our existing analyses and considering additional controls where appropriate.

      Statistical modelling and stimulus characteristics

      We will also revisit the statistical modelling of trial-level behavioural associations, including the potential contribution of item-level variability. We recognise the reviewers' suggestion to examine psycholinguistic properties of the naming stimuli and will assess whether the available stimulus characteristics permit informative supplementary analyses, while acknowledging that the task was not designed to independently manipulate these variables.

    1. eLife Assessment

      This is an important study that establishes a novel and enabling 3D super-resolution imaging approach for visualizing the nanoscale organization of AMPA receptors in thick mouse brain tissue. The biological findings are of importance and interest to the neuroscience field, with potential implications for neurodegenerative disease research. Some of the biological conclusions would benefit from strengthened quantitative analysis and revisions to the manuscript to ensure that the interpretations are appropriately aligned with what is supported by the analysis.

    2. Reviewer #1 (Public review):

      Summary:

      This study presents a workflow for nanoscale imaging of endogenous synaptic receptors in intact adult brain tissue. By combining live-tissue chemical labeling of native surface AMPA receptors (AMPARs) using the CAM2 probe, two-color 3D super-resolution microscopy, adaptive optics, in situ point spread function retrieval, and chromatic aberration correction, the authors establish a platform for quantitative super-resolution mapping of synaptic architecture deep within brain tissue.

      The authors provide convincing evidence that AMPAR organization differs between the hippocampus and cortex and demonstrate that early tauopathy is associated with selective disruption of extrasynaptic and synaptic nanodomain receptor organization in the hippocampus prior to overt neurodegeneration.

      Beyond the specific biological findings, the work represents a significant technical advance that enables quantitative investigation of endogenous synaptic receptor architecture in native brain tissue and will be of broad interest to researchers in synaptic biology, super-resolution microscopy, and neurodegeneration.

      Strengths:

      (1) Introduces and validates a strategy for nanoscale imaging of endogenous surface AMPARs in adult brain slices using CAM2 labeling and two-color 3D super-resolution imaging.

      (2) Achieves 10s of nanometers localization precision in thick tissue and validates synaptic structural measurements against previous literature benchmarks.

      (3) Reveals regional differences in AMPAR organization, including a larger extrasynaptic receptor pool in hippocampal regions compared with neighboring cortical regions.

      (4) Identifies early, region-specific alterations in AMPAR distribution and nanodomain organization in the PS19 tauopathy model.

      Weaknesses:

      (1) Statistical power is limited by the relatively small number of animals analyzed, although large numbers of synaptic structures were sampled within each mouse.

      (2) Synaptic classification relies on PSD-95 cluster detection criteria that may bias analysis toward more strongly enriched synapses, as acknowledged by the authors.

    3. Reviewer #2 (Public review):

      Summary:

      This is a technically sophisticated effort to visualize the nanoscale organization of native surface AMPA receptors in adult mouse brain. The authors combine chemical labeling of endogenous AMPARs with 3D, 2-color super-resolution microscopy. They use this to compare hippocampal and cortical AMPAR organization and to characterize changes in a P301S mutant mouse tauopathy model. The authors report a greater density of surface AMPAR localizations in hippocampal CA1 than in two examined cortical regions, as well as a smaller fraction of AMPARs organized in synaptic clusters. They report that PS19 tauopathy is associated with reduced hippocampal AMPAR localization density, a relative depletion of the population classified as extrasynaptic, and reduced organization of receptors within synaptic nanodomains.

      Strengths:

      The methodological contribution is substantial. The authors developed a valuable set of methods for examining endogenous surface AMPAR organization in brain tissue. The imaging methodology could be useful across many questions in synaptic neuroscience.

      Weaknesses:

      While the biological observations are interesting, several aspects of the neuroscience interpretation require clarification, and some of the biological conclusions should be weakened unless the measurements can better justify them. I provide several comments intended to strengthen this interesting manuscript.

      (1) The authors measure fluorescent localization counts but often interpret these as AMPAR numbers. I believe that individual fluorophores can generate multiple localizations and that labeling efficiency can vary, challenging their frequent interpretation as AMPAR numbers. Importantly, AMPARs are tetrameric complexes with potentially different subunit compositions, so one AMPAR could be observed as 0-4 fluorescent localizations. The uncertainty is particularly important for comparisons of AMPAR abundance per cluster and nanodomain. The authors should consistently distinguish localization density from receptor number and clarify which conclusions depend on assumptions about labeling and the assumptions of photophysical equivalence between experimental conditions. The paper would be more rigorous and the claims more legitimate if the authors were disciplined throughout about using empirical descriptors instead of interpretations.

      (2) The distinction between synaptic and extrasynaptic receptors is central to the manuscript. The authors identify synaptic receptors either through density-based clustering or proximity to PSD-95 clusters. However, neither measure directly establishes anatomical synaptic localization. This is particularly consequential because only ~1/3 of morphologically identified YFP-positive spines contain PSD-95 clusters that meet the detection criteria. It seems to me that AMPARs associated with weakly-labeled or undetected synapses could then be erroneously classified as extrasynaptic. PSD-95 detection should be assessed across regions and genotypes, and a sensitivity analysis undertaken to ensure that the conclusions are robust to the choices of clustering and distance thresholds.

      (3) The authors suggest that the larger extrasynaptic AMPAR population in the hippocampus provides a reserve for rapid synaptic recruitment and that its reduction in tauopathy constrains plasticity. This is plausible, but receptor abundance does not establish receptor mobility or availability for synaptic capture. The importance of AMPAR surface diffusion for hippocampal LTP and contextual learning has been demonstrated (see Penn et al., Nature, 2017), but the current study does not measure diffusion or receptor exchange. The authors should temper their interpretation and explain the caveats.

      (4) The authors infer that there has been a preferential extrasynaptic AMPAR loss from the reduced total AMPAR localization density and the increased synaptic fraction they measured. Because both synaptic and extrasynaptic populations could decrease even when the synaptic fraction increases, they should calculate the synaptic and extrasynaptic localization densities separately for each mouse. This would clarify the magnitude and selectivity of the changes that they report and help to reconcile the potentially contradictory increased synaptic fraction with reduced AMPAR localization density within synaptic clusters and nanodomains.

      (5) The authors adjust DBSCAN parameters for PS19 hippocampus in response to reduced overall localization density. Because the resulting clustering measurements are themselves used to establish genotype differences, it is important to demonstrate that the conclusions are robust to parameter selection. Analyses using identical parameter sets across conditions, together with simulations in which WT localization patterns are subsampled to match PS19 localization density, would help determine whether the reported changes reflect biological reorganization rather than density-dependent detection effects. Similar validation is important for the nanodomain analysis. I found that, in general, the authors should have made a greater effort to ensure that their measurements are robust to the parameter choices.

      (6) CA1 comparisons with motor/somatosensory cortex require more precise anatomical characterization. What cortical layers and hippocampal dendritic compartments are being compared? These are not homogeneous structures in almost any regard. AMPAR or, more empirically, fluorescence localization density per imaging field is not necessarily equivalent to AMPAR density per dendritic membrane area or per synapse. Differences in neuropil composition, spine density, and synapse density could contribute to the observed differences when there is, in fact, no AMPAR density per dendritic membrane area. Proper anatomical normalization or a stricter comparison of equivalent neuronal compartments would strengthen the interpretation. I believe that the authors are aware of the issue, as they did an analysis along a single dendrite, but this has to be done more thoroughly for them to justify the conclusions they assert.

      (7) The main comparisons involve three mice per group, and the authors correctly analyze mouse-level averages. Nonetheless, the small number of biological replicates limits the strength of the conclusions about regional specificity and disease effects. The test statistics and degrees of freedom, not just the p-values, should be presented. In addition, effect sizes and individual mouse values should be presented. The absence of a statistically significant cortical effect does not establish that cortex is unaffected. There could be genotype-by-region interactions, which the authors should check.

      (8) The authors describe that 6-mo PS19 mice precede neurodegeneration and they assert that the observed AMPAR-associated changes relate to the onset of synaptic dysfunction. However, Yoshiyama et al., Neuron, 2007 demonstrate hippocampal synapse loss and impaired synaptic function at three months in PS19 mice, before detecting fibrillary tau. The present observations likely occur after synaptic pathology in thePS19 mice. The authors should distinguish structural correlates of established dysfunction from the mechanisms that may have initiated that dysfunction. More importantly, the assertion that neuronal or synaptic anatomy is preserved in the PS19 mice should be supported by appropriate measurements.

      (9) The reduction of the AMPAR nanodomain organization is potentially important. However, the authors do not measure presynaptic release sites or trans-synaptic alignment. To be rigorous, the data establish altered postsynaptic receptor organization but do not demonstrate disruption of trans-synaptic nanocolumns or establish the functional consequences for quantal transmission. If the authors do not provide evidence for these claims they should not make them and make it clear these are (reasonable) speculations for which they did not collect the data.

    4. Author response:

      Reviewer #1 (Public review):

      Summary:

      This study presents a workflow for nanoscale imaging of endogenous synaptic receptors in intact adult brain tissue. By combining live-tissue chemical labeling of native surface AMPA receptors (AMPARs) using the CAM2 probe, two-color 3D super-resolution microscopy, adaptive optics, in situ point spread function retrieval, and chromatic aberration correction, the authors establish a platform for quantitative super-resolution mapping of synaptic architecture deep within brain tissue.

      The authors provide convincing evidence that AMPAR organization differs between the hippocampus and cortex and demonstrate that early tauopathy is associated with selective disruption of extrasynaptic and synaptic nanodomain receptor organization in the hippocampus prior to overt neurodegeneration.

      Beyond the specific biological findings, the work represents a significant technical advance that enables quantitative investigation of endogenous synaptic receptor architecture in native brain tissue and will be of broad interest to researchers in synaptic biology, super-resolution microscopy, and neurodegeneration.

      Strengths:

      (1) Introduces and validates a strategy for nanoscale imaging of endogenous surface AMPARs in adult brain slices using CAM2 labeling and two-color 3D super-resolution imaging.

      (2) Achieves 10s of nanometers localization precision in thick tissue and validates synaptic structural measurements against previous literature benchmarks.

      (3) Reveals regional differences in AMPAR organization, including a larger extrasynaptic receptor pool in hippocampal regions compared with neighboring cortical regions.

      (4) Identifies early, region-specific alterations in AMPAR distribution and nanodomain organization in the PS19 tauopathy model.

      We thank the reviewer for reviewing our manuscript and appreciating both the neurological findings, as well as the broader technological advance.

      Weaknesses:

      (1) Statistical power is limited by the relatively small number of animals analyzed, although large numbers of synaptic structures were sampled within each mouse.

      We agree that despite sampling a large number of synaptic structures and imaging fields per mouse, a sample size of n=3 mice per genotype is a limitation of the study. We will highlight this point in the discussion section.

      (2) Synaptic classification relies on PSD-95 cluster detection criteria that may bias analysis toward more strongly enriched synapses, as acknowledged by the authors.

      We do agree and acknowledge in the manuscript that PSD-95 cluster detection may bias the synaptic fraction analysis towards more enriched synapses. In addition, we will also compare PSD-95 cluster detection across brain regions and genotypes.

      Reviewer #2 (Public review):

      Summary:

      This is a technically sophisticated effort to visualize the nanoscale organization of native surface AMPA receptors in adult mouse brain. The authors combine chemical labeling of endogenous AMPARs with 3D, 2-color super-resolution microscopy. They use this to compare hippocampal and cortical AMPAR organization and to characterize changes in a P301S mutant mouse tauopathy model. The authors report a greater density of surface AMPAR localizations in hippocampal CA1 than in two examined cortical regions, as well as a smaller fraction of AMPARs organized in synaptic clusters. They report that PS19 tauopathy is associated with reduced hippocampal AMPAR localization density, a relative depletion of the population classified as extrasynaptic, and reduced organization of receptors within synaptic nanodomains.

      Strengths:

      The methodological contribution is substantial. The authors developed a valuable set of methods for examining endogenous surface AMPAR organization in brain tissue. The imaging methodology could be useful across many questions in synaptic neuroscience.

      We thank the reviewer for reviewing our manuscript and appreciating the imaging methodology developed in this manuscript.

      Weaknesses:

      While the biological observations are interesting, several aspects of the neuroscience interpretation require clarification, and some of the biological conclusions should be weakened unless the measurements can better justify them. I provide several comments intended to strengthen this interesting manuscript.

      We thank the reviewer for the in-depth comments which will help strengthen our manuscript. We provide our response to each of them below.

      (1) The authors measure fluorescent localization counts but often interpret these as AMPAR numbers. I believe that individual fluorophores can generate multiple localizations and that labeling efficiency can vary, challenging their frequent interpretation as AMPAR numbers. Importantly, AMPARs are tetrameric complexes with potentially different subunit compositions, so one AMPAR could be observed as 0-4 fluorescent localizations. The uncertainty is particularly important for comparisons of AMPAR abundance per cluster and nanodomain. The authors should consistently distinguish localization density from receptor number and clarify which conclusions depend on assumptions about labeling and the assumptions of photophysical equivalence between experimental conditions. The paper would be more rigorous and the claims more legitimate if the authors were disciplined throughout about using empirical descriptors instead of interpretations.

      We agree that due to the photo-physical behavior of the fluorophore combined with the tetrameric AMPAR composition and labeling efficiency variations, the AMPAR localizations should not be directly interpreted as absolute AMPAR numbers. We will standardize the wording to “AMPAR localizations” instead of “number of AMPARs / receptors” used at times in the Result and Discussion sections. We will also add an explicit statement of the labeling and photophysical assumptions and of which comparisons depend on photophysical equivalence across conditions. Because all samples are prepared, labeled and imaged identically, relative differences in localization density remain valid. We already note the requirements for absolute AMPAR counting in the Discussion section and will expand that caveat.

      (2) The distinction between synaptic and extrasynaptic receptors is central to the manuscript. The authors identify synaptic receptors either through density-based clustering or proximity to PSD-95 clusters. However, neither measure directly establishes anatomical synaptic localization. This is particularly consequential because only ~1/3 of morphologically identified YFP-positive spines contain PSD-95 clusters that meet the detection criteria. It seems to me that AMPARs associated with weakly-labeled or undetected synapses could then be erroneously classified as extrasynaptic. PSD-95 detection should be assessed across regions and genotypes, and a sensitivity analysis undertaken to ensure that the conclusions are robust to the choices of clustering and distance thresholds.

      We agree with the concern and acknowledge in the manuscript that PSD-95 cluster detection may bias the synaptic fraction analysis towards more enriched synapses. We also thank the reviewer for additional analysis suggestions. We will include a comparison of PSD-95 cluster detection across regions and genotypes. We note that in fig. S7, we already compare two AMPAR-PSD-95 distance thresholds used to define the synaptic region, showing that the differences in synaptic fraction across regions and genotypes are insensitive to small changes in the threshold choice. In addition, we will perform a broader sensitivity analysis of the proximity-based synaptic fraction measurement across varying distance thresholds and PSD-95 clustering parameters.

      (3) The authors suggest that the larger extrasynaptic AMPAR population in the hippocampus provides a reserve for rapid synaptic recruitment and that its reduction in tauopathy constrains plasticity. This is plausible, but receptor abundance does not establish receptor mobility or availability for synaptic capture. The importance of AMPAR surface diffusion for hippocampal LTP and contextual learning has been demonstrated (see Penn et al., Nature, 2017), but the current study does not measure diffusion or receptor exchange. The authors should temper their interpretation and explain the caveats.

      We agree. Our data measures spatial distributions and not diffusion or receptor exchange. We will present the reserve-pool interpretation (a pool available for rapid synaptic recruitment) explicitly as a hypothesis motivated by prior work rather than a conclusion, and add the caveat that diffusion or exchange measurements would be needed to test it.

      (4) The authors infer that there has been a preferential extrasynaptic AMPAR loss from the reduced total AMPAR localization density and the increased synaptic fraction they measured. Because both synaptic and extrasynaptic populations could decrease even when the synaptic fraction increases, they should calculate the synaptic and extrasynaptic localization densities separately for each mouse. This would clarify the magnitude and selectivity of the changes that they report and help to reconcile the potentially contradictory increased synaptic fraction with reduced AMPAR localization density within synaptic clusters and nanodomains.

      We thank the reviewer for this suggestion. We will include a comparison of synaptic and extrasynaptic densities calculated separately for each mouse. This will certainly help clarify the magnitude and selectivity of the reported changes.

      (5) The authors adjust DBSCAN parameters for PS19 hippocampus in response to reduced overall localization density. Because the resulting clustering measurements are themselves used to establish genotype differences, it is important to demonstrate that the conclusions are robust to parameter selection. Analyses using identical parameter sets across conditions, together with simulations in which WT localization patterns are subsampled to match PS19 localization density, would help determine whether the reported changes reflect biological reorganization rather than density-dependent detection effects. Similar validation is important for the nanodomain analysis. I found that, in general, the authors should have made a greater effort to ensure that their measurements are robust to the parameter choices.

      We will separate the two requested analysis, which serve different purposes.

      (i) Identical parameters across conditions: we show in Fig. S9 that identical parameters under-detect AMPAR clusters in the lower density PS19 hippocampus, which is precisely why we scaled the DBSCAN parameters for AMPAR cluster analysis in PS19 hippocampus. Moreover, the proximity-based synaptic fraction measurement, which is independent of any AMPAR cluster analysis, was performed with identical distance thresholds and PSD-95 cluster detection parameters across regions and genotypes. It shows an increase in synaptic fraction in PS19 hippocampus (Fig. 4f).

      (ii) Density-matched subsampling: we agree this is a key control. We will randomly down-sample AMPAR localization in wild-type hippocampus to match the localization density with PS19 hippocampus, and then re-run AMPAR cluster analysis. This will help identify PS19 changes that reflect genuine reorganization and those that reflect density-dependent effects.

      (6) CA1 comparisons with motor/somatosensory cortex require more precise anatomical characterization. What cortical layers and hippocampal dendritic compartments are being compared? These are not homogeneous structures in almost any regard. AMPAR or, more empirically, fluorescence localization density per imaging field is not necessarily equivalent to AMPAR density per dendritic membrane area or per synapse. Differences in neuropil composition, spine density, and synapse density could contribute to the observed differences when there is, in fact, no AMPAR density per dendritic membrane area. Proper anatomical normalization or a stricter comparison of equivalent neuronal compartments would strengthen the interpretation. I believe that the authors are aware of the issue, as they did an analysis along a single dendrite, but this has to be done more thoroughly for them to justify the conclusions they assert.

      We thank the reviewer for highlighting this important concern. We agree that CA1 hippocampus and cortex are not homogeneous structures. We will state the compartments explicitly in the revision. Stratum radiatum layer was imaged in the CA1 region of the hippocampus. In cortex, layer V pyramidal neurons are labeled with YFP in Thy1-YFP-H mice and their apical dendritic field was imaged in layers II/III and IV.

      In the paper, we show AMPAR cluster analysis across the whole imaging field of view, as well as analysis restricted to the AMPAR localizations along a YFP dendrite for comparison. In addition, we will also report membrane area-normalized AMPAR localization density as well as the spine density across brain regions and genotypes.

      (7) The main comparisons involve three mice per group, and the authors correctly analyze mouse-level averages. Nonetheless, the small number of biological replicates limits the strength of the conclusions about regional specificity and disease effects. The test statistics and degrees of freedom, not just the p-values, should be presented. In addition, effect sizes and individual mouse values should be presented. The absence of a statistically significant cortical effect does not establish that cortex is unaffected. There could be genotype-by-region interactions, which the authors should check.

      We agree that despite sampling a large number of synaptic structures and imaging fields per mouse, a sample size of n=3 mice per genotype is a limitation of the study. We will further highlight this point in the discussion section.

      We already report all the individual mouse values plotted in Fig. 3-4, in Tables S5, S10 and S11. In addition, we will report test statistics, degrees of freedom and effect sizes. We will also test for genotype-by-region interactions.

      (8) The authors describe that 6-mo PS19 mice precede neurodegeneration and they assert that the observed AMPAR-associated changes relate to the onset of synaptic dysfunction. However, Yoshiyama et al., Neuron, 2007 demonstrate hippocampal synapse loss and impaired synaptic function at three months in PS19 mice, before detecting fibrillary tau. The present observations likely occur after synaptic pathology in thePS19 mice. The authors should distinguish structural correlates of established dysfunction from the mechanisms that may have initiated that dysfunction. More importantly, the assertion that neuronal or synaptic anatomy is preserved in the PS19 mice should be supported by appropriate measurements.

      Yoshiyama et al., Neuron, 2007 report a decline of synaptophysin and α-synuclein immunoreactivity at 3 months of age, a decline of GluR2/3 immunoreactivity at 6 months of age and reduced basal synaptic transmission and CA1 LTP at 6 months of age in PS19 mice. Whereas the filamentous tau lesions were reported to be developed at 6 months of age and overt neuron loss and brain atrophy was reported by 9-12 months of age. In our manuscript, we already note that functional deficits have been reported by Yoshiyama et al. and others by the 6-months age and do not wish to claim that our reported changes precede synaptic pathology and dysfunction. Our precedence statement is relative to the overt neurodegeneration and brain atrophy that is reported at later stages. We will make this distinction more explicit and update any wording that could be read as framing the AMPAR changes as preceding synaptic pathology and dysfunction. In addition, we will also compare the YFP-labeled spine density across brain regions and genotypes.

      (9) The reduction of the AMPAR nanodomain organization is potentially important. However, the authors do not measure presynaptic release sites or trans-synaptic alignment. To be rigorous, the data establish altered postsynaptic receptor organization but do not demonstrate disruption of trans-synaptic nanocolumns or establish the functional consequences for quantal transmission. If the authors do not provide evidence for these claims they should not make them and make it clear these are (reasonable) speculations for which they did not collect the data.

      We agree that our data establishes altered postsynaptic receptor organization, but does not directly demonstrate the disruption of trans-synaptic nanocolumn. We will restrict our wording to postsynaptic receptor organization and present any statement about nanocolumns or quantal efficacy explicitly as reasonable speculation for which we did not collect any data.

    1. eLife Assessment

      This study presents a valuable finding on the condition dependence of autophagy-mediated lifespan regulation in C. elegans. The evidence is solid, as the data broadly support the main claims, although variability between biological replicates in several of the performed experiments and mechanistic exploration with only a limited set of experimental techniques leave some conclusions less firmly established. The work will be of interest to researchers studying autophagy, ageing, and intracellular trafficking.

    2. Reviewer #1 (Public review):

      Summary:

      Hsiung et al. investigated whether the effects of autophagy gene knockdown on the lifespan of long-lived C. elegans mutants depend on experimental conditions. The authors first compiled published data on autophagy-dependent lifespan regulation in daf-2 and wild-type backgrounds, highlighting that prior results are notably inconsistent and likely context-dependent. They then systematically tested the lifespan effects of RNAi knockdown of six autophagy genes (atg-2, atg-4.1, atg-9, atg-13, atg-18, and bec-1) in wild-type (N2), daf-2 (reduced insulin/IGF-1 signalling), and glp-1 (germlineless) animals, while varying temperature, daf-2 allele, FUDR concentration, and bacterial infection status.

      The key findings are as follows. In wild-type animals, lifespan suppression by most autophagy gene knockdowns was more pronounced at 20{degree sign}C than at 25{degree sign}C, where little or no effect was observed. In daf-2 mutants, stronger lifespan suppression was seen in the weaker daf-2(e1368) allele at 20{degree sign}C, but not in the stronger daf-2(e1370) allele, and effects were largely absent at 25{degree sign}C. In glp-1 mutants, four of six gene knockdowns suppressed lifespan to a greater extent than in N2, though again in a temperature-dependent manner. FUDR at a high concentration (800 µM) abolished the life-shortening effects of most knockdowns and, in the case of atg-9 and atg-13, led to lifespan extension. Kanamycin treatment to eliminate bacterial proliferation did not fully account for the lifespan effects, suggesting that increased susceptibility to infection is not the primary mechanism. The authors also tested the programmed aging hypothesis that autophagy promotes lifespan reduction through biomass repurposing, but found no changes in vitellogenin levels upon knockdown of any of the six genes.

      Altogether, among all genes tested, atg-18 knockdown produced the strongest and most consistent lifespan suppression across nearly all conditions, including both daf-2 and glp-1 backgrounds. The authors probed whether atg-18 acts through the FOXO transcription factor DAF-16 by examining dauer formation and ftn-1 expression, but found no evidence for this, suggesting a DAF-16-independent mechanism.

      Strengths:

      The primary strength of this work lies in its systematic and comprehensive approach to dissecting how experimental variables influence the outcome of autophagy-lifespan epistasis tests. The compilation of prior data alongside the authors' own multi-condition dataset is a genuinely useful resource for the field. The study raises a timely and important point about condition selection bias in interpreting autophagy lifespan relationships, with broader relevance to C. elegans studies. The finding that atg-18 behaves distinctly from other autophagy genes across a range of experimental conditions is particularly noteworthy and provides an interesting direction for future mechanistic investigation.

    3. Reviewer #2 (Public review):

      Summary:

      This study examines how genes involved in cellular recycling (autophagy) influence lifespan under different experimental conditions. The findings help clarify why previous studies have reported conflicting results about whether blocking autophagy shortens or extends lifespan. The work will be of interest to researchers studying aging and cellular stress responses, particularly those using model organisms.

      Strengths:

      The findings are valuable, as they help resolve inconsistencies within a specific subfield of aging research. The evidence presented is solid, as the data broadly support the primary claims of the study. In addition, the discussion is thorough and thoughtfully integrates the findings within the broader context of the field.

    4. Author response:

      The following is the authors’ response to the previous reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      Hsiung et al. investigated whether the effects of autophagy gene knockdown on the lifespan of long-lived C. elegans mutants depend on experimental conditions. The authors first compiled published data on autophagy-dependent lifespan regulation in daf-2 and wild-type backgrounds, highlighting that prior results are notably inconsistent and likely context-dependent. They then systematically tested the lifespan effects of RNAi knockdown of six autophagy genes (atg-2, atg-4.1, atg-9, atg-13, atg-18, and bec-1) in wild-type (N2), daf-2 (reduced insulin/IGF-1 signalling), and glp-1 (germlineless) animals, while varying temperature, daf-2 allele, FUDR concentration, and bacterial infection status.

      The key findings are as follows. In wild-type animals, lifespan suppression by most autophagy gene knockdowns was more pronounced at 20{degree sign}C than at 25{degree sign}C, where little or no effect was observed. In daf-2 mutants, stronger lifespan suppression was seen in the weaker daf-2(e1368) allele at 20{degree sign}C, but not in the stronger daf-2(e1370) allele, and effects were largely absent at 25{degree sign}C. In glp-1 mutants, four of six gene knockdowns suppressed lifespan to a greater extent than in N2, though again in a temperature-dependent manner. FUDR at a high concentration (800 µM) abolished the life-shortening effects of most knockdowns and, in the case of atg-9 and atg-13, led to lifespan extension. Kanamycin treatment to eliminate bacterial proliferation did not fully account for the lifespan effects, suggesting that increased susceptibility to infection is not the primary mechanism. The authors also tested the programmed aging hypothesis that autophagy promotes lifespan reduction through biomass repurposing, but found no changes in vitellogenin levels upon knockdown of any of the six genes.

      Altogether, among all genes tested, atg-18 knockdown produced the strongest and most consistent lifespan suppression across nearly all conditions, including both daf-2 and glp-1 backgrounds. The authors probed whether atg-18 acts through the FOXO transcription factor DAF-16 by examining dauer formation and ftn-1 expression, but found no evidence for this, suggesting a DAF-16-independent mechanism.

      Strengths:

      The primary strength of this work lies in its systematic and comprehensive approach to dissecting how experimental variables influence the outcome of autophagy-lifespan epistasis tests. The compilation of prior data alongside the authors' own multi-condition dataset is a genuinely useful resource for the field. The study raises a timely and important point about condition selection bias in interpreting autophagy lifespan relationships, with broader relevance to C. elegans studies. The finding that atg-18 behaves distinctly from other autophagy genes across a range of experimental conditions is particularly noteworthy and provides an interesting direction for future mechanistic investigation.

      Comments on revised version.

      The authors have carefully addressed the concerns raised in the previous review and have incorporated most of the suggested revisions. In particular, the revised manuscript provides greater clarity regarding the effects of glp-1 and FUDR and improves the presentation and interpretation of the experimental findings. The authors have also provided useful clarification regarding the variability between lifespan experiments, the number of biological replicates, and the interpretation of RNAi efficacy.

      I have only two minor suggestions concerning the wording and organization of the manuscript, which I have communicated separately to the authors. These points are intended to further improve clarity and accuracy and do not affect my overall assessment of the study.

      Overall, I am satisfied with the revisions and consider the manuscript to be a valuable contribution to our understanding of the context dependence of autophagy-mediated lifespan regulation.

      Reviewer 1 made several further suggestions for improvements to the text that we agree with.

      Reviewer #2 (Public review):

      Summary:

      This study examines how genes involved in cellular recycling (autophagy) influence lifespan under different experimental conditions. The findings help clarify why previous studies have reported conflicting results about whether blocking autophagy shortens or extends lifespan. The work will be of interest to researchers studying aging and cellular stress responses, particularly those using model organisms.

      Strengths:

      The findings are valuable, as they help resolve inconsistencies within a specific subfield of aging research. The evidence presented is solid, as the data broadly support the primary claims of the study. In addition, the discussion is thorough and thoughtfully integrates the findings within the broader context of the field.

      Weaknesses:

      Additional functional validation would further strengthen the conclusions.

      Comments on revised version.

      I have reviewed the revised manuscript. Overall, the authors have addressed most of my concerns, and the revised manuscript has been improved. The study provides a comprehensive examination of the context-dependent effects of autophagy gene knockdown on lifespan in C. elegans. The data are solid and provide valuable information for understanding the conflicting results currently reported in the literature regarding the role of autophagy in longevity.Recommendations for the authors:

      Reviewer 2 correctly noted that measurements of autophagic flux would strengthen our claims. We had originally planned to include flux measurements in this study, but our experience with the methodologies involved led us to a separate critical study of such methodologies that we are in the process of writing up.

      Recommendations for the authors:

      Reviewing Editor Comments:

      The manuscript could still be strengthened by taking into account the points raised by Reviewer 2, in specific the validation of autophagy with different techniques, such as western blotting and potentially raise the strength of evidence from solid to convincing.

      Reviewer #1 (Recommendations for the authors):

      I would like to thank the authors for carefully addressing most of my previous concerns in their revised manuscript and incorporated most of the suggestions. Most of my concerns have been adequately addressed. However, I have two minor points that, in my opinion would further improve the clarity and accuracy of the manuscript.

      We are glad that our revisions have at least addressed most of the reviewer’s concerns.

      The authors have appropriately incorporated glp-1 into the abstract and have clarified the statement regarding FUDR. However, in the current version, these two statements still feel somewhat disconnected from the overall flow of the abstract, as they are inserted in the middle of the discussion of the effects observed in daf-2. This interrupts the logical progression of the paragraph and may make it slightly more difficult for the reader to follow the main findings regarding daf-2. I would suggest moving these two statements after the main findings concerning daf-2 have been presented. This would allow the abstract to first provide a coherent summary of the daf-2 results and then introduce the additional findings regarding FUDR and glp-1. I believe this would improve the overall readability and narrative flow of the abstract.

      We have revised the abstract along these lines and agree that this improves the flow of the argument. We have used the revised abstract.

      In reply to the 10th point, I appreciate the authors' clarification that their primary focus is on the suppression of class I mutants rather than the effects on N2. However, I believe the revised sentence still remains slightly misleading "These findings are broadly consistent with the earlier observation that bec-1 and vps-34 RNAi shortened the lifespan of the daf-2(mu150) class 1 mutant but not of N2 at 20˚C". As presented in the current manuscript, the cumulative dataset indicates that bec-1 RNAi does significantly suppress N2 lifespan. Therefore, the phrase "but not of N2 at 20{degree sign}C" is not accurate in the context of the present study, even if the authors' main point is that the effect on the class 1 daf-2 mutant is of particular interest. I would therefore suggest rephrasing this sentence to avoid implying that bec-1 RNAi has no effect on N2 lifespan.

      We agree that this account was rather too broad brush, and have corrected it to improve precision, adding the following. “(though in the present study bec-1 RNAi did also reduce N2 lifespan, Table S2)”

      Overall, I am satisfied with the authors' responses and the revisions made to the manuscript. Addressing these two minor points would further improve the clarity and accuracy of the manuscript.

      Reviewer #2 (Recommendations for the authors):

      I have reviewed the revised manuscript. Overall, the authors have addressed most of my concerns, and the revised manuscript has been improved. The study provides a comprehensive examination of the context-dependent effects of autophagy gene knockdown on lifespan in C. elegans. The data are solid and provide valuable information for understanding the conflicting results currently reported in the literature regarding the role of autophagy in longevity.

      We are glad that our revisions have at least addressed most of the reviewer’s concerns.

      One of my major concerns from the first review, however, has not been fully addressed. The authors rely primarily on qPCR to demonstrate the effectiveness of RNAi-mediated knockdown of autophagy genes. Although qPCR data confirm transcriptional knockdown, they do not necessarily demonstrate a corresponding reduction in protein levels or functional inhibition of autophagy. An independent approach, such as Western blot analysis of lipidated LGG-1/ATG-8, would make the conclusions more convincing. Nevertheless, given the overall quality and consistency of the data, I do not consider this issue sufficient to prevent publication.

      Assays of autophagy would indeed have been informative. We did initially plan to test effects of atg RNAi on autophagic flux using a dual fluorescence reporter. However, initial tests identified complex technical issues with the assay that have taken us several years to make sense of (we have almost finished writing up this work). A Western blot approach might have been less problematic, at least in terms of estimating effects on lipidated LGG-1/ATG-8 level. However, any changes could, at least in principle, reflect either decreased or increased autophagic flux.

      The findings of this study also raise an important issue for future studies of autophagy and longevity, particularly when RNAi is used to manipulate autophagy gene expression. RNAi methodology can vary considerably among laboratories, and these differences may contribute to apparently conflicting results. Different laboratories may use different RNAi constructs targeting the same gene. Some investigators generate their own RNAi plasmids using either genomic DNA or cDNA fragments, and the sizes and locations of the targeted sequences can differ. Other laboratories use RNAi clones obtained from established C. elegans RNAi libraries, such as the Ahringer or Vidal libraries. Such differences could affect knockdown efficiency.

      In addition, the method used to deliver RNAi should be considered when comparing results among studies. Most laboratories use feeding RNAi, whereas some studies used injection RNAi. Injection RNAi can produce substantially stronger gene knockdown than feeding RNAi in many experimental settings. Thus, differences in the RNAi delivery method, together with differences in the RNAi constructs themselves, could result in different degrees of gene inhibition and potentially different effects on lifespan. This issue may be particularly important for autophagy genes, for which the biological consequences could depend on the extent of gene inhibition. For these reasons, it would be valuable for future studies in this area to clearly report the source and characteristics of the RNAi constructs used, as well as the method of RNAi delivery.

      This is all correct, and is something that we give little emphasis to in the manuscript, since it relates to possible sources of condition dependency that we did not investigate. Most studies use RNAi by feeding and specify when it is initiated (see Supplementary Table 1). In our tests we opted to initiate RNAi at the end of development, to avoid life shortening effects of disruptions to development.

      Overall, the manuscript presents solid and valuable data that help further our understanding of the role of autophagy in longevity.

    1. eLife Assessment

      This valuable study reports the architectural reorganization of the uterine luminal epithelium during the implantation period. Solid data are presented to support the main conclusion. This work is of interest to reproductive biologists and physicians practicing reproductive medicine.

    2. Reviewer #1 (Public review):

      Summary:

      This manuscript asks how the uterine lumen is remodeled across the peri-implantation window and whether this remodeling is functionally linked to embryo attachment and subsequent pregnancy establishment. The authors combine whole-organ three-dimensional imaging of optically cleared mouse uteri with single-cell and spatial transcriptomic profiling, conditional deletion of p38α at the uterine-wide versus epithelial-restricted level, and rescue experiments using progesterone and leukemia inhibitory factor. Based on these datasets, the authors propose that the luminal epithelium undergoes a previously underappreciated phase of organ-scale architectural reorganization before attachment, and that a p38α-dependent stress-responsive program coordinates epithelial remodeling together with epithelial-stromal communication required for implantation competence.

      Strengths:

      By moving beyond local attachment-site morphology to a horn-level representation of luminal topology, the work provides anatomical context that is difficult to reconstruct from conventional section-based approaches and should be broadly useful to the implantation community. The integration of organ-scale morphology with single-cell and spatial transcriptomic datasets, together with genetic perturbation and rescue experiments, adds breadth and increases the potential long-term utility of the dataset for investigators interested in uterine receptivity and embryo-uterine interactions.

      The revision has addressed several important concerns from the original review. The added methodological details and comparison with conventional two-dimensional histology provide greater confidence that the luminal features observed by whole-organ imaging are not artifacts of tissue clearing or reconstruction. The additional measurements of epithelial folding and orientation also provide better quantitative support for luminal remodeling. The mechanistic interpretation of the p38α findings has also been improved.

      Weaknesses:

      Most of the major concerns raised in the original review have been addressed. I have only a few remaining comments.

      First, the authors have appropriately toned down the causal language regarding embryo positioning. However, because different animals were analyzed at different time points, the direction of the relationship between luminal remodeling and embryo position cannot be determined. A few statements still sound somewhat causal, such as luminal movement "contributes to embryo spacing" and persistent epithelial branching "led to randomized embryo positioning." These could be further softened to reflect associations rather than causality.

      Second, some mechanistic claims could also be more cautious. For example, the Figure 9 title states that embryo invasion failure occurs "owing to sustained epithelial polarity." The persistence of E-cadherin and β-catenin is consistent with altered epithelial polarity, but does not establish it as the cause of invasion failure. "Associated with sustained epithelial polarity" would be more appropriate.

      Third, the added quantitative analyses are helpful, but the individual animal should remain the biological replicate. When multiple sections from the same dam are analyzed, these should not be treated as independent biological replicates.

      Finally, the Figure 9c legend refers to β-actin, whereas the Methods and figure indicate β-catenin. This should be corrected.

    3. Reviewer #2 (Public review):

      Summary:

      In this study, the authors investigate luminal architectural reorganization during embryo implantation. Using 3D histological reconstruction, single-cell RNA sequencing, spatial transcriptomics, and genetic mouse models, the authors the uterine luminal epithelium undergoes rapid morphogenetic remodeling during the implantation. They further propose that a stress-responsive p38α signaling program coordinates epithelial remodeling and epithelial-stromal communication to establish an implantation-competent uterine lumen.

      Strengths:

      This study clearly described the changes of luminal architecture during implantation. Moreover, they also used integration of multiple advanced techniques, including 3D tissue reconstruction, single-cell transcriptomics, spatial transcriptomics, and genetic mouse models, which together provide a detailed description of the molecular characters of the uterine architecture during implantation.

      Weaknesses:

      The principal knockout model uses Pgr-Cre, which deletes p38α in both epithelial and stromal compartments. Importantly, they also added epithelial-specific knockout (Ltf-Cre) demonstrates that epithelial deletion alone produces only mild abnormalities and does not recapitulate the severe implantation phenotype observed in the uterine knockout. These data suggest that the critical function of p38α likely resides in stromal cells or in epithelial-stromal interactions. Consequently, the manuscript's title and central conclusion currently a little bit overstate epithelial regulation.

      The data of this study are now support the main conclusions.

      Impact of the work on the field:

      This work will likely become an important resource for researchers studying embryo implantation because it provides a detailed atlas of peri-implantation uterine morphology integrated with spatial and single-cell transcriptomic datasets. The imaging methodology and quantitative description of luminal remodeling are likely to be widely useful.

    4. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This manuscript asks how the uterine lumen is remodeled across the peri-implantation window and whether this remodeling is functionally linked to embryo attachment and subsequent pregnancy establishment. The authors combine whole-organ three dimensional imaging of optically cleared mouse uteri with single-cell and spatial transcriptomic profiling, conditional deletion of p38α at the uterine-wide versus epithelial-restricted level, and rescue experiments using progesterone and leukemia inhibitory factor. Based on these datasets, the authors propose that the luminal epithelium undergoes a previously underappreciated phase of organ-scale architectural reorganization before attachment, and that a p38α-dependent stress responsive program coordinates epithelial remodeling together with epithelial-stromal communication required for implantation competence.

      Strengths:

      By moving beyond local attachment-site morphology to a horn-level representation of luminal topology, the work provides anatomical context that is difficult to reconstruct from conventional section-based approaches and should be broadly useful to the implantation community. The integration of organ-scale morphology with single-cell and spatial transcriptomic datasets, together with genetic perturbation and rescue experiments, adds breadth and increases the potential long-term utility of the dataset for investigators interested in uterine receptivity and embryo-uterine interactions.

      We thank the reviewer for the positive comments and constructive suggestions.

      Weaknesses:

      (1) The whole-uterus analysis of luminal folds and creases requires stronger methodological support. Given the mechanical compliance of the uterine lumen, it is difficult to evaluate from the current description whether dissection, fixation, clearing, and/or mounting could influence the observed luminal topography. This issue is particularly important because several key conclusions depend on the spatial distribution of folds across the uterine horn. A fuller account of tissue handling and reconstruction, together with validation that the preparation preserves native morphology, would substantially strengthen confidence in the organ-scale conclusions.

      We thank the reviewer for raising this important methodological concern.

      To address this concern, we have expanded the Methods section to provide a fuller description of tissue preparation, image acquisition, three-dimensional reconstruction, and quantitative analysis. Three-dimensional visualization of the uteri collected on days 1–4 and of implantation sites collected on days 5 and 6 was performed as previously described (PMID: 42022843). We have also clarified how representative cross-sectional images were selected from each reconstructed dataset and how identical analysis parameters were applied across samples.

      Specifically, three representative two-dimensional cross-sectional images were generated from each three-dimensional reconstructed dataset. From day 4 onward, sections were obtained at the position of the embryo and at positions ±500 μm from the embryo. For samples collected at earlier stages, sections were obtained at the midpoint of the image stack and at positions ±500 μm along the longitudinal axis. Epithelial staining area and branching features were then quantified from these cross-sectional images using the same analysis criteria for all samples. These methodological details have been added to the revised Methods section (Page 27, Line 619).

      In addition, to assess whether the whole-mount preparation preserved the overall luminal topography, we added representative transverse and longitudinal sections obtained using conventional two-dimensional histological analysis as Supplementary Fig. 1. The characteristic luminal morphology observed in these sections was comparable to that observed in the corresponding three-dimensional reconstructions. Although this comparison cannot completely exclude all possible effects of tissue processing, it supports the conclusion that the major morphological features identified by whole-organ imaging were not artifacts introduced by clearing, mounting, or reconstruction. The corresponding explanation has been added to the Results section (Page 8, Line 166).

      (2) Several of the central morphological claims are supported primarily only by representative reconstructions. This includes the proposed flattening/creasing dynamics, alternating stretched and shrunken regions, persistence of abnormal folding in the mutant uterus, and the extent of structural rescue following progesterone supplementation. The authors could extract objective measures from the reconstructed luminal surface and provide more statistical analysis to demonstrate the reproducibility of the results.

      We appreciate this constructive suggestion and agree that objective morphometric analyses are necessary to support the conclusions derived from the representative three-dimensional reconstructions.

      Quantitative analyses of luminal epithelial flattening during days 1–4, differences between p38α uterine control and knockout mice, and morphological changes following P4/LIF administration to p38α uterine knockout mice were already included in Figures 1d and 7c. In the revised manuscript, we have expanded the descriptions of these analyses and clarified the experimental units and sample numbers in the figure legends.

      We also performed additional quantitative analyses to characterize the alternating shrunken and stretched regions. We measured the orientation of luminal epithelial folds relative to the mesometrial–antimesometrial axis. In wild-type mice on day 4 at 20:00, folds in the regions surrounding embryos were predominantly vertically oriented, whereas folds at inter-implantation regions were predominantly horizontally oriented. These results provide an objective criterion distinguishing the areas that we refer to as “shrunken” and “stretched” and are now presented in Figure 2e.

      We performed the same analysis in control and p38α-deficient uteri and found that the spatially organized transition between vertically and horizontally oriented folds was disrupted in the mutant uterus. These results are now presented in Figure 7e and quantitatively support the persistence of abnormal luminal folding in p38α-deficient mice.

      In addition, we measured the angle of the luminal epithelium relative to the longitudinal uterine axis at three positions: the embryo center and positions 30 μm on either side of the center. The average of the three measurements was calculated for each biological sample. This analysis revealed a significant difference between day 4 at 20:00, before stable attachment, and day 4 at 24:00, after attachment, and is now presented in Figure 2f.

      Together with the existing measurements of epithelial area and branching features, these additional analyses provide quantitative support for the reproducibility of the luminal flattening, folding, and regional remodeling phenotypes. Nevertheless, we acknowledge that the reconstructed surface contains additional features, such as local curvature and fold depth, that could be examined in greater detail in future studies.

      (3) The manuscript appears to over-reach in concluding that luminal remodeling zones embryos before attachment from day 4 to 5. As presented, the data support a correlation between luminal architecture and embryo position, but do not discriminate between (i) luminal remodeling directing embryo positioning, (ii) embryos locally shaping the lumen, or (iii) parallel regulation of both. The evidence is based on observations of the uterus and the inside blastocysts at certain time points around implantation. Without the time-lapse analysis within the uterus, the dynamic interactions between embryos and the uterus couldn't be determined.

      We agree with the reviewer that our data demonstrate a strong spatial and temporal association between luminal architecture and embryo position but do not establish the direction of causality.

      Our analyses were performed at defined time points during the peri-implantation period and therefore cannot distinguish whether uterine luminal remodeling directs embryo positioning, whether embryos locally induce changes in luminal morphology, or whether both processes are coordinately regulated by common uterine and embryonic signals. As the reviewer correctly notes, direct determination of these dynamic interactions would require longitudinal or time-lapse imaging of embryos and the uterine lumen within the same animal.

      We have therefore revised the manuscript to moderate the causal language concerning embryo “zoning.” We now describe the findings as showing that regional luminal remodeling is associated with, and may contribute to, the spatial organization of embryos before attachment, rather than concluding that luminal remodeling directly determines embryo position. We have also added an explicit statement that the directionality of the interaction between embryo position and luminal remodeling remains unresolved in the present study. The relevant text in the Discussion has been revised accordingly (Page 19, Line 444).

      (4) The key conclusion of the manuscript is that uterine p38α regulates luminal epithelial remodeling required for embryo attachment, as shown in the title. Against this background, the finding that epithelial-restricted loss of p38α does not overtly impair fertility is notable, as it suggests that the major function of p38α may not be epithelial cell-autonomous but instead may arise through other uterine compartments that secondarily influence the epithelium. At present, however, this conclusion remains insufficiently supported: the epithelial-specific model is not characterized in sufficient depth during the peri-implantation period, and the transcriptomic evidence for altered epithelial-stromal communication does not by itself explain the phenotypic difference between uterine-wide and epithelial-specific deletion. If stromal p38α is proposed as the critical upstream regulator, more direct testing, such as stromal-specific deletion, would be needed.

      We thank the reviewer for highlighting this important point. We agree that the mild phenotype observed following epithelial-specific deletion of p38α argues against a solely epithelial cell-autonomous mechanism and that our current data do not directly demonstrate that stromal p38α is the exclusive upstream regulator of luminal epithelial remodeling.

      The uterine p38α knockout mice generated using Pgr-Cre lack p38α in multiple uterine compartments, including epithelial and stromal cells. To assess the contribution of epithelial p38α, we generated epithelial-specific p38α knockout mice using the Ltf-Cre driver. These mice did not exhibit the complete infertility or severe luminal remodeling abnormalities observed in the Pgr-Cre model and showed only mild uterine abnormalities. These results indicate that epithelial deletion alone is insufficient to reproduce the severe phenotype caused by broader uterine deletion of p38α.

      We have revised the Results and Discussion sections to describe the epithelial-specific knockout analysis more clearly and to distinguish between conclusions directly supported by the genetic models and the proposed mechanism of epithelial–stromal communication (Page 15, Line 344; Page 20, Line 459). We also clarify that stromal- or decidual-cell-specific deletion of p38α will be required to directly determine whether stromal p38α is necessary for luminal remodeling, embryo attachment, and subsequent embryo invasion. These experiments represent an important direction for future investigation.

      Reviewer #2 (Public review):

      Summary:

      In this study, the authors aimed to characterize the architectural reorganization of the uterine luminal epithelium during the implantation period. Using 3D histological reconstruction, single-cell RNA sequencing, and spatial transcriptomics, the authors characterize luminal remodeling during the peri-implantation period and employ a mouse model to explore the role of p38α in regulating luminal flattening.

      Strengths:

      This study clearly described the changes in luminal architecture during implantation. Moreover, they also used integration of multiple advanced techniques, including 3D tissue reconstruction, single-cell transcriptomics, and spatial transcriptomics, which together provide a detailed description of the molecular characteristics of the uterine architecture during implantation.

      We thank the reviewer for the positive comments and constructive suggestions.

      Weaknesses:

      The authors used PR-Cre to generate uterine p38α knockout mice. This Cre driver deletes p38α not only in epithelial cells but also in stromal compartments. Therefore, it remains unclear whether the observed phenotype arises from epithelial cells, stromal cells, or a combination of both. Previous studies have shown that p38α regulates epithelial polarity, cytoskeletal organization, and E-cadherin localization. However, the current study does not examine changes in cell adhesion or epithelial junction integrity. Previous studies have reported that uterine fluid absorption during implantation is closely associated with luminal closure and remodeling. It would be important to determine whether epithelial transport-related genes are altered in the mutant uterus. Could dysregulated fluid homeostasis contribute to the implantation defects observed in the p38α-deficient mice?

      We thank the reviewer for identifying these important mechanistic issues. We agree that the Pgr-Cre driver deletes p38α in multiple uterine compartments, including both epithelial and stromal cells, and therefore does not by itself establish which compartment is primarily responsible for the observed phenotype.

      To assess the contribution of epithelial p38α, we generated epithelial-specific p38α knockout mice using the Ltf-Cre driver. These results are presented in Supplementary Fig. S6. In contrast to the complete infertility and severe luminal remodeling defects observed in Pgr-Cre-mediated uterine p38α knockout mice, epithelial-specific deletion of p38α did not critically impair female fertility and caused only mild uterine abnormalities. These findings indicate that epithelial loss of p38α alone is insufficient to reproduce the severe phenotype observed in the Pgr-Cre model.

      We have revised the Results and Discussion sections to describe the Ltf-Cre analysis more clearly and to emphasize that the phenotype of the Pgr-Cre model is unlikely to result solely from epithelial-autonomous loss of p38α (Page 15, Line 344; Page 20, Line 459). Nevertheless, we agree that these findings do not directly establish stromal p38α as the exclusive upstream regulator. The phenotype may reflect the combined effects of p38α deletion in multiple uterine compartments and disrupted communication between epithelial and stromal cells. Direct testing using stromal- or decidual-cell-specific deletion will therefore be required to determine the precise contribution of stromal p38α.

      We also agree that epithelial adhesion, polarity, and cytoskeletal organization are relevant to the luminal remodeling phenotype. In the original study, we examined E-cadherin and β-catenin, which are associated with adherens junctions and epithelial polarity. In the luminal epithelium of day 4 p38α uterine knockout mice, staining for E-cadherin and β-catenin was stronger and more persistent than in control mice. These findings do not suggest a simple loss of epithelial junction integrity. Instead, they are consistent with impaired remodeling of adhesion- and polarity-associated epithelial structures during the peri-implantation period.

      We have revised the Results and Discussion sections to interpret these findings more explicitly in the context of epithelial adhesion and polarity. We now discuss the possibility that defective remodeling of epithelial junctional or cytoskeletal structures contributes to the persistence of abnormal luminal folding. However, because epithelial-specific p38α deletion did not reproduce the severe phenotype of the Pgr-Cre model, these epithelial abnormalities may arise, at least in part, from altered signaling between epithelial and non-epithelial uterine compartments rather than from an exclusively epithelial-autonomous defect.

      Finally, in response to the reviewer’s suggestion concerning uterine fluid homeostasis, we re-examined our RNA-seq dataset with a focus on genes associated with epithelial transport, barrier function, and fluid regulation. Several transport- and barrier-associated genes were altered in the p38α-deficient uterus, including Aqp3, Slc26a6, Cldn10, Gjb2, Trpv6, Trpm6, and Atp6v0a4. These genes are involved in processes such as water transport, ion transport, paracellular permeability, intercellular communication, and pH or ion homeostasis.

      These transcriptomic changes raise the possibility that dysregulated epithelial transport and fluid homeostasis contribute to defective luminal closure and remodeling in p38α-deficient mice. We have added this interpretation to the revised manuscript. However, because gene-expression analysis does not directly measure transporter activity, uterine fluid volume, or fluid absorption, our current data do not establish a causal relationship between altered fluid homeostasis and implantation failure. We have therefore presented this mechanism as a possibility and have added direct functional analysis of uterine fluid absorption and epithelial transport as an important direction for future study.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      The main need for the current manuscript is to apply greater analytical rigor and sharper framing.

      We thank the reviewer for the positive comments and constructive suggestions.

      (1) Provide a fuller description of dissection, orientation, fixation, clearing, mounting, acquisition settings, and reconstruction/segmentation criteria, including quality-control steps and exclusion criteria.

      Thank you for your advice. We added these sentences in the method sections (Page27, Line619). 3D visualization of the days 1–4 uteri or day 5 and 6 implantation sites was performed as previously reported (PMID 42022843).

      To quantitatively evaluate epithelial flattening morphology, three representative 2D cross-sectional images were generated from each 3D reconstructed dataset. Sections were collected at the site of embryo located and its ±500 μm positions on day 4 onwards, otherwise midpoint of the image stack and its ±500 μm positions along the longitudinal (x) axis. Epithelial staining area and epithelial branching features were subsequently quantified from these cross-sectional images using identical analysis parameters across samples.

      These detailed methods are now added into the revised manuscript.

      Add validation that the whole-uterus preparation preserves the luminal original physiological topography.

      We appreciate this advice. Representative transverse and longitudinal sections were added as Supplementary Fig. 1 to confirm that luminal morphology was comparable between conventional two-dimensional section analysis and three-dimensional evaluation. The main text was revised accordingly (Page8, Line 166).

      (2) Strengthen the support for the whole-uterus morphology data by adding quantitative morphometric analysis across biological replicates. Metrics related to fold density, epithelial branching, curvature, surface roughness, or related parameters would make the central morphological conclusions more rigorous.

      Thank you for this advice.

      Quantification of luminal flattening from days 1 to day 4 in wild-type mice, comparisons between p38α uCtrl and uKO mice, and the morphological changes induced by LIF/P4 administration in p38α uKO mice have already been presented in Figures 1d and 7c. We now added detailed explanation about these data.

      To further quantify the transition between shrunken and stretched areas in wild-type mice before and after embryo attachment, we quantified the angle of luminal foldings against M-AM axis, showing shrunken areas have vertical folding (around 0 degree) while stretched areas have horizontal folding (around 90 degree) on day 4 20:00 (Figure 2e), which were dysregulated in p38α-deleted uteri (Figure 7e). We additionally measured the angle of the luminal epithelia against the longitudinal axis at three positions (the embryo center and ±30 μm from the center) using ImageJ. The average of these three measurements was calculated for each sample (n = 3). This analysis revealed a significant difference between day 4 20:00 (pre-attachment) and day 4 24:00 (post-attachment) (Figure 2f), indicating that embryo location is moved in alignment with the time course.

      (3) Define the experimental unit clearly in each quantitative analysis. In several panels, it is not fully clear whether n refers to animals, uterine horns, implantation sites, sections, or embryos.

      Thank you for pointing this out. Now the definition of n has been clarified in the figure legend.

      (4) The causal language around embryo zoning should be moderated unless additional evidence can be provided. As written, the data support a strong association between luminal architecture and embryo position, but not yet a directional mechanism.

      Thank you for this opinion. According to this suggestion, we have revised the Discussion to avoid overstating this conclusion (Page19, Line 444).

      (5) Figure presentation should be revised for consistency and clarity. For example:- Scale bars and their labeling should be standardized throughout.

      - In Figure 4e, the representative uterine photographs do not appear to be shown at a fully matched scale between each other.

      - In Figure 4f, the control and mutant three-dimensional views appear to be displayed from different angles, which makes comparison of crypt formation and depth more difficult. As these are three-dimensional datasets, matched viewpoints should be feasible.

      - Figure 9c appears to be mislabeled. The panel is labeled as β-actin, but based on the main context, this should be β-catenin.

      Thank you for pointing this out. We have corrected Figures 4e, 4f, and 9c as suggested.

      (6) The apparent reuse of the uKO image between Figures 4e and 5h should be clarified. If the same image is intentionally reused, this should be stated explicitly; otherwise, a different representative image would be preferable.

      Thank you for pointing this out. As you indicated, the same image had been inadvertently reused. We sincerely apologize this mistake and have replaced it with an alternative representative image in figure 5h.

      (7) Please check the figure legends and Methods carefully for internal consistency, including panel labels, marker names, genotype names, sampling time points, and nomenclature.

      Thank you for your advice. We have standardized the notation of gene names, protein names, and other terminology throughout the manuscript.

      (8) The Methods should include a clear description of the epithelial-specific p38α deletion model.

      We apologize for the lack of explanation. We have added a description of how the epithelial-specific p38α knockout mice were generated in the Methods section.

      (9) The manuscript requires thorough editing for language and presentation. For example:

      - From line 53 to 5: "In the uterus, proliferation-differentiation switching (PDS), indicating the inhibition of epithelial proliferation with induction of stromal proliferation in the endometrium, is evident on day 4 under the continuous influence of P4, resulting in the endometrium acquiring implantation potential."

      - From lines 257 to 261, the content of two consecutive sentences is repeated: "Based on these results, we examined whether activation of Lif-Stat3 can restore the flawed embryo attachment in p38α uKO. These results prompted us to determine whether supplementation of Lif along with P4 can ameliorate abnormal implantation in p38α uKO.

      Thank you for your valuable advice. We have revised the sentences accordingly and further refined the manuscript through professional English editing.

      Reviewer #2 (Recommendations for the authors):

      (1) The authors used PR-Cre to generate uterine p38α knockout mice. This Cre driver deletes p38α not only in epithelial cells but also in stromal compartments. Therefore, it remains unclear whether the observed phenotype arises from epithelial cells, stromal cells, or a combination of both. An epithelial-specific deletion model would help clarify the cell-type-specific role of p38α in luminal remodeling.

      We thank the reviewer for raising this important point. We agree that Pgr-Cre deletes p38α in multiple uterine compartments, including epithelial and stromal cells, and that this point should be clearly addressed when interpreting the phenotype.

      To clarify the contribution of the epithelial compartment, we have already generated and analyzed epithelial-specific p38α knockout mice using the Ltf-Cre driver. These data are shown in Supplementary Fig. S6. In contrast to the complete infertility and severe luminal remodeling defects observed in Pgr-Cre-mediated uterine p38α knockout mice, epithelial-specific deletion of p38α did not critically impair female fertility and caused only mild uterine abnormalities. Thus, epithelial deletion alone is not sufficient to recapitulate the severe phenotype observed in the Pgr-Cre model.

      We agree that this point was not sufficiently emphasized in the original manuscript. We have therefore revised the Results and Discussion sections to explicitly describe the Ltf-Cre analysis and to clarify that the severe phenotype in Pgr-Cre mutants is unlikely to arise solely from an epithelial-autonomous loss of p38α. Instead, our data support a model in which p38α in non-epithelial uterine compartments, particularly the stroma, contributes to luminal epithelial remodeling through epithelial–stromal communication.

      We have also softened the wording to avoid implying that the Pgr-Cre model alone definitively identifies the stromal compartment as the exclusive source of the phenotype. Rather, the combination of the Pgr-Cre phenotype, the mild Ltf-Cre phenotype, and the single-cell/spatial transcriptomic analyses supports a critical role for p38α-dependent epithelial–stromal crosstalk in establishing implantation-competent luminal architecture.

      We now added explanations regarding eKO in the result sections (Page15, Line344) as well as Discussion sections (Page20, Line459)

      (2) Previous studies have shown that p38α regulates epithelial polarity, cytoskeletal organization, and E-cadherin localization. However, the current study does not examine changes in cell adhesion or epithelial junction integrity. The authors should evaluate markers of cell adhesion and polarity and discuss whether the observed phenotype could result from impaired epithelial junctions or altered cytoskeletal organization.

      We agree with the reviewer that epithelial adhesion, polarity, and cytoskeletal organization are important potential mechanisms underlying the luminal remodeling phenotype. In the original manuscript, we examined E-cadherin and β-catenin, both of which are associated with adherens junctions and epithelial polarity. We observed stronger and more persistent staining of these markers in day 4 p38α uKO luminal epithelium compared with controls, suggesting impaired remodeling of epithelial adhesion/polarity-associated structures rather than a simple loss of epithelial junctions.

      We have revised the Results and Discussion sections to explicitly describe these data in the context of epithelial adhesion and polarity. We further discuss that altered junctional or cytoskeletal remodeling may contribute to the persistent luminal folding phenotype. However, because epithelial-specific p38α deletion using Ltf-Cre did not reproduce the severe phenotype of Pgr-Cre-mediated uterine knockout mice, we interpret these epithelial abnormalities as likely arising, at least in part, from disrupted epithelial–stromal communication rather than from an exclusively epithelial-autonomous junctional defect.

      (3) Previous studies have reported that uterine fluid absorption during implantation is closely associated with luminal closure and remodeling. It would be important to determine whether epithelial transport-related genes are altered in the mutant uterus. Could dysregulated fluid homeostasis contribute to the implantation defects observed in the p38α-deficient mice?

      We thank the reviewer for this thoughtful suggestion. As the reviewer noted, uterine fluid absorption and epithelial transport are important processes associated with luminal closure and remodeling during implantation.

      In response to the reviewer’s suggestion, we re-examined our RNA-seq dataset with a focus on genes related to epithelial transport and fluid homeostasis. We found that several transport- and barrier-associated genes were altered in the P4/LIF-untreated p38a-deficient uterus, including Aqp3, Slc26a6, Cldn10, Gjb2, Trpv6, Trpm6, and Atp6v0a4. These genes are associated with water transport, ion transport, paracellular permeability, epithelial communication, and pH/ion homeostasis. These findings suggest that dysregulated epithelial transport and fluid homeostasis may contribute, at least in part, to impaired luminal closure/remodeling and implantation defects in the mutant uterus.

      We have added this point to the revised manuscript. Nevertheless, because transcriptomic data do not directly measure transporter activity or uterine fluid dynamics, further functional studies will be required to determine whether altered fluid absorption directly contributes to the abnormal luminal architecture. We have revised the Discussion to acknowledge this limitation and to propose direct functional analysis of uterine fluid absorption and epithelial transport as an important future direction.

      (4) It is intriguing that P4 plus LIF supplementation rescues embryo attachment, but pregnancy still fails at later stages. The authors should discuss whether this defect might result from p38α deletion in stromal cells or from persistent abnormalities in luminal remodeling.

      Thank you for your advice. It is difficult to determine from the current study whether the observed phenotype is primarily caused by persistent luminal epithelial abnormalities or by stromal p38α deficiency. In the present study, P4 and LIF supplementation restored embryo attachment in p38α uKO mice; however, embryo invasion remained completely absent, resulting in subsequent pregnancy failure. Previous studies from our group have shown that uterine deficiency of Hif2α, Rb1, Ezh2, or Lox impairs embryo invasion and leads to pregnancy failure, highlighting the importance of coordinated epithelial–stromal remodeling during implantation (PMID: 41803091, 3719814, 2012472, 33399260). In the p38α uKO uterus, although P4 and LIF supplementation restored key molecular signals required for attachment, abnormalities in luminal morphology persisted. Therefore, persistent defects in luminal remodeling may contribute to the failure of embryo invasion and subsequent pregnancy progression. However, because embryo attachment is severely impaired in untreated p38α uKO mice, the post-attachment role of stromal p38α cannot be directly assessed in the current model. Further studies using decidual cell-specific genetic approaches will be required to clarify the contribution of stromal p38α to embryo invasion. We added these sentences in the discussion sections.

      (5) The terminology describing luminal morphogenesis requires clarification, particularly the definitions of "shrunken areas" and "stretched areas." These terms should be clearly defined and, if possible, supported by quantitative criteria

      Thank you for your suggestion. To quantify the transition between shrunken and stretched areas in wild-type mice, we measured the angle of luminal folding around the embryo (±500 µm) as well as inter-implantation sites on day 4 2000h using ImageJ. This analysis revealed the epithelia around the embryos are more shrunken with vertically oriented folding compared to inter-implantation sites (Figure 2e). We similarly analyzed uteri from control and p38α uKO, and found uKO poorly formed shrunken and stretched areas, which may compromise embryo implantation in this milieu (Figure 7e).

      (6) The exact collection time points for day 4 and day 5 uteri should be clearly specified (Line 169), as implantation-related changes are highly time-dependent.

      Thank you for your advice. We have added “10:00” to both day 4 and day 5.

      (7) The manuscript relies heavily on qualitative visual comparisons of luminal morphology. The authors should provide quantitative morphometric analyses, such as measurements of lumen width, epithelial curvature, or fold depth, to support their conclusions.

      Thank you for your suggestion. To quantify the transition between shrunken and stretched areas in wild-type mice before and after embryo attachment, we measured the angle between the LE folding adjacent to the embryo and inter-implantation sites against the M-AM axis. The corresponding results have been added to the manuscript (Figure 2e and 7e).

    1. eLife Assessment

      This work presents valuable findings that identify ergosterol binding by the human ABC transporter ABCG5/G8 and show that ergosterol stimulates its ATPase activity more strongly than cholesterol, advancing understanding of sterol recognition and transport. The evidence from single-particle cryo-EM, molecular dynamics simulations, and biochemical assays is convincing, although additional structures and mutational studies would strengthen the proposed mechanism. These results provide key insight into the molecular basis of sterol recognition and regulation of ABCG5/G8 and will be of interest to researchers studying sterol transport and ABC transporter function.

    2. Reviewer #1 (Public review):

      Summary:

      In this manuscript by Rezaei F. et al., the authors determine a cryo-EM structure of the human ABCG5/G8 ABC transporter that was expressed in Pichia Pastoris. The structure was nearly identical to previous crystal and cryo-EM structures of ABCG5/G8, and displayed a non-protein density in the central transport pocket between both halves of the transporter. Since the expression host Pichia pastoris natively makes ergosterol rather than cholesterol, and cholesterol hemisuccinate (CHS) was added during protein solubilization and purification, the authors used molecular dynamics simulations to probe the stability of binding for cholesterol, CHS, and ergosterol in the central pocket where the density was identified. These simulations suggested that ergosterol, rather than CHS, occupies the central pocket in the cryo-EM structure.

      The authors then went on to investigate the effect of ergosterol on ABCG5/G8 ATPase activity. Interestingly, ergosterol was more potent than cholesterol or CHS in stimulating ABCG5/G8 ATPase activity. It was further demonstrated that the hydroxyl moiety of Y342 in ABCG5 is essential to achieve full stimulation of ATPase activity by ergosterol, but not cholesterol. This assay highlights a key residue (Y342) in ABCG5 that contributes to differential sterol sensitivity of ABCG5/G8.

      Overall, this is a compelling story with solid biochemical kinetic data that largely support the interpretation that binding of ergosterol gives rise to the sterol-like density in the transport pocket of ABCG5/G8.

      Strengths:

      (1) The finding that ABCG5/G8 responds to ergosterol more strongly than cholesterol is very interesting. This suggests a plausible role for ABCG5/G8 in regulating absorption of exogenous sterols from the human diet.

      (2) The biochemical kinetics data appear to be quite strong, with appropriate replicates and statistical analyses in most locations.

      (3) The cryo-EM density map appears to be of good quality despite an overall resolution of ~3.6 Å.

      (4) The manuscript and figures are well prepared and easy to follow.

      Weaknesses:

      (1) While I agree with the conclusion that the central density is likely not CHS, how can the authors be sure that this is not cholate (which was included throughout solubilization and purification)?

      (2) The authors do comment on the role of the hydroxyl on Y432 being important for differential regulation of the transporter by sterols. However, it was not clear to me how the hydroxyl of Y342 could contribute to enhanced activity with ergosterol and not cholesterol. These two molecules are very similar and bind in essentially the exact same location. Could it be that the additional double bond (creating a conjugated double-bond system) in the B-ring of ergosterol interacts with Y342 in a favorable manner that is not possible with cholesterol, which lacks this double bond? It seems like more discussion on the chemical similarities and differences between ergosterol and cholesterol is warranted in order to potentially explain why the Y342 hydroxyl moiety is so critical for stimulation with ergosterol but not cholesterol.

      (3) Minor weakness: Enthusiasm for the cryo-EM structure is slightly tempered by the fact that the structure is essentially identical to a previous ABCG5/G8 cryo-EM structure bound to cholesterol.

      (4) I am a little confused by Figure 1D. If the RMSD is calculating the ligand position relative to its initial position in the first frame, then shouldn't the RMSD value for all ligands start at zero when the time is zero? Why does RMSD not start at 0 for all ligands and then gradually increase as the simulation progresses? Similarly, why is the starting RMSD at time 0 so different for ergosterol, cholesterol, and CHS?

      (5) It would be good to include a "no sterol" control for the experiments in Figure 3B. Is the basal ATPase activity without sterols the same for WT and Y342F constructs?

      (6) In Figure 4D and Table 1 (bottom), ATPase activity is measured as a function of sterol concentration, and the data are fit using a Michaelis-Menten equation, with Vmax and Km reported. I do not think that interpreting these parameters as Vmax and Km is appropriate for this experiment. Although a saturating Michaelis-Menten-type equation can be used to describe the dependence of ATPase activity on sterol concentration, sterol is not the substrate being directly measured in the ATPase reaction. Rather, the experiment measures sterol-dependent stimulation of ATP hydrolysis. Thus, these data are more appropriately described as a sterol dose-response or activation curve, and an apparent EC50 would be a more appropriate parameter to report. A true Km for sterol would require measurement of the sterol transport reaction as a function of sterol concentration.

      (7) It would be good to include statistical analyses of the interaction energy decompositions from molecular dynamics (Figure 2C) where appropriate. For instance, is the difference between interaction energies for (G5)-Y424 really different for cholesterol and ergosterol? The error bar for ergosterol makes it hard to tell if this difference is statistically significant.

      (8) The changes in side-chain orientations noted in the paper (Figure 2B) are really very, very small changes. I am not quite sure that these represent true changes due to ligand binding, versus small changes in model refinement into intermediate resolution range maps (~3.6Å cryoEM and ~4Å crystal structure).

      (9) The discussion surrounding an alternative sterol binding pocket, and particularly its involvement in sterol flip-flop (Figure 4B), seems very speculative.

      (10) The authors included AMP-PNP and MgCl2 in their sample for cryo-EM, but did not observe a closed state in 3D classification. Any speculation as to why the open conformation was obtained, or, more importantly, why AMP-PNP was included at all?

    3. Reviewer #2 (Public review):

      Summary:

      Rezaei et al. determined the cryo-electron microscopy structure of human ABCG5/8 in complex with ergosterol, revealing a conserved sterol-binding site at the transmembrane domain (TMD) interface, signifying the role of a specific key residue, Y432, in the interaction. ATPase assays showed the effective stimulation of ATPase activity of ABCG5/8 in the presence of ergosterol than cholesterol and CHS. The role of Y432 in the interaction with the transporter and stimulation of its ATPase activity was further supported by a mutagenesis study.

      Strengths:

      The study has been supported well with the structural and biochemical data combined with molecular dynamics simulations. The methods are very well detailed, which is helpful for the reader.

      Weaknesses:

      A few more experiments along similar lines (structures and mutagenesis and ATPase experiments) would have strengthened the molecular mechanism of sterol transport. Also, the physiological relevance of this sterol-ABCG5/8 structure has not been elaborated or connected via any experiments.

    4. Reviewer #3 (Public review):

      Summary:

      This study provides structural and functional insights into the sterol recognition mechanism of ABCG5/G8.

      Strengths:

      The modeling of ergosterol into the density map is plausible, considering that the protein was heterologously expressed in yeast, only CHS was added during purification, and MD simulations rendered CHS binding to this site unlikely. The identified ergosterol-binding site in the central TMD cavity closely aligns with the location previously observed in a cholesterol-soaked crystal structure, strongly supporting the notion that this cavity serves as the primary substrate-transport pocket. Furthermore, mutagenesis of the key Tyr residue abolishes the ergosterol-specific enhancement of ATPase activity, providing robust functional support for the structural findings.

      Weaknesses:

      However, several caveats should be considered when interpreting these results:

      First, while local resolution in the TMD region appears sufficient to trace side chains, the overall resolution of 3.5 Å poses challenges for precisely discerning subtle side-chain movements or rotamer changes. As the structural comparison with the apo and CRL-bound forms reveals no major helical shifts-remaining confined to side-chain rearrangements-caution is warranted when attributing mechanistic significance to these localized variations without exhaustive functional validation.

      Second, a major limitation lies in the mechanistic model proposed by the authors. The manuscript suggests that the peripheral cholesterol-binding site (CRL) observed exclusively in previous crystal structures represents an intermediate state of sterol flip-flop. However, the authors provide no new experimental or computational evidence in this study to substantiate this hypothesis. Consequently, this model remains speculative and lacks sufficient empirical backing at this stage.

    5. Author response:

      We thank the editors and reviewers for their careful evaluation of our manuscript and their constructive comments. We are pleased that the reviewers found the identification of a sterol-binding cavity in ABCG5/G8, the stronger stimulation of ATPase activity by ergosterol, and the role of ABCG5<sub>Y432</sub> in differential sterol activation to be of interest to the greater transporter community.

      Reviewer #1:

      (1) We agree that cholate should be considered, as it was present throughout protein purification. We acknowledge that at the current resolution, the identity of the bound ligand cannot be determined unambiguously from the cryo-EM density alone, particularly among sterol-like molecules of similar size and shape. For this reason, we described the cavity as a potential sterol-binding site and ergosterol as the most likely ligand and did not rely on the structure alone to define sterol-protein interactions through this cavity. Here, we combined ligand fitting, molecular dynamics simulations, site-directed mutagenesis, and in vitro functional assays to evaluate the role of this cavity in sterol selectivity. In the revision, we will fit cholate into the density, compare its fit with those of ergosterol, cholesterol, and CHS, and include MD simulation data for cholate as described for these molecules. Furthermore, previous studies showed that cholate only contributed to the basal ATPase activity [1, 2], so we will include ATPase data for the ABCG5<sub>Y432F</sub> mutant under sterol-free conditions and compare it with WT under the same conditions, since our assays included cholate.

      (2) In the current manuscript, we discuss how the hydroxyl group of Y432 may provide an additional interaction capability within the binding cavity. In the revision, we will add a discussion of the chemical differences between ergosterol and cholesterol, including the double bond in the B ring and the additional double bond and methyl group in the side chain, and how these features may favor interactions with the Y432 hydroxyl and aromatic ring.

      (3) We agree that the overall fold is similar to previous ABCG5/G8 structures. We want to clarify that this is the first structural evidence showing a non-cholesterol bound to the central cavity and its link to sterol-specific activation of the transporter’s catalysis.

      (4) The ligand RMSD values were calculated relative to the docked starting poses. Because the systems underwent equilibration and the first 25 ns of each production simulation were excluded from analysis, the ligands had already relaxed from their initial poses at the start of the analyzed trajectories, which explains why the values do not start at zero and differ among sterols. We will clarify this in the Methods and figure legend in the revision.

      (5) We thank the reviewer for this suggestion. The basal activity of WT ABCG5/G8 is currently shown in Figure 3A. In the revision, we will measure the basal activity of ABCG5<sub>Y432F</sub> under the same sterol-free conditions and add both WT and Y432F sterol-free controls to Figure 3B for direct comparison.

      (6) We agree that sterol acts as an activator rather than the directly measured substrate in this assay. In the revision, we will describe these data (Figure 3D and Table 1) as sterol-dependent activation curves and report apparent EC50 and maximal activity values in place of Km and Vmax.

      (7) We thank the reviewer for bringing up this important point. In the original submission, Fig. 2C shows per-residue MMGBSA decomposition as the mean +/- SD of three independent replicates. We performed a Welch’s t-test for all three pairs with a Benjamini-Hochberg FDR for all 48 comparisons, the residues which had mentioned were in direction but were not statistically significant. This remains relevant since our simulations showed that in all replicates, CHS would move away from the docked site which explains why the standard deviations for the MMGBSA calculations were so large. We equally re-analyzed each trajectory from the point where CHS’s RMSD stabilized which equally yielded large SD binding free energy decomposition, however, no residues passed the BH FDR. For this reason, we will modify our language within the text to reflect the comparisons between sterol groups as trends rather than significant differences.

      (8) We agree that these side chain differences are small relative to the resolution of the structures. We will revise the text to describe them more cautiously.

      (9) We agree that the proposed role of the surface-exposed site in sterol flip-flop is speculative. The model is based on the cholesterol molecule observed at the ABCG8 to ABCG5 transmembrane interface in our previous crystal structure (PDB ID: 8CUB) [3]. We discuss this site as having a possible role in sterol sensing and regulation or in sterol movement between membrane leaflets, as suggested for other ABC transporters [4]. We acknowledge that neither role is directly tested in this study. In the revision, we will rephrase this section, clearly present both possibilities as hypotheses for future investigation, and revise Figure 4B and its legend to indicate that it represents a proposed model.

      (10) AMPPNP and MgCl<sub>2</sub> were included to match the conditions under which we previously obtained a cholesterol-bound structure of ABCG5/G8 [3]. Our aim was to capture the sterol-bound state of the transporter rather than an ATP hydrolysis or nucleotide-bound closed state. Consistent with our previous structure, no nucleotide density was observed in the nucleotide binding sites, and the transporter adopted an inward-facing conformation. We will clarify this rationale in the Methods of the revised manuscript and discuss possible role of nucleotide in this report.

      Reviewer #2:

      We agree that additional structures and mutants would further strengthen the mechanistic model, and we will discuss these as directions for future work. We will also expand the Discussion on the physiological relevance of differential sterol recognition by ABCG5/G8, including dietary non-cholesterol sterols.

      Reviewer #3:

      (1) We agree that the observed side chain differences should be interpreted with caution given the resolution. We will revise the text to avoid attributing mechanistic significance to these localized variations beyond what is supported by our mutagenesis and functional data.

      (2) We agree that the present study does not provide new experimental or computational evidence that the CRL site represents an intermediate in sterol flip-flop. This model is based on the cholesterol-bound site observed in our previous crystal structure (PDB ID: 8CUB) [3], and in the current manuscript we present it as one of two possible roles for this site, alongside sterol sensing or regulation. In the revision, we will state the basis of the model more clearly, present both possibilities as hypotheses for future investigation, and revise the related text and Figure 4B accordingly (see also our response to Reviewer #1, point 9).

      We look forward to submitting a revised manuscript that addresses these points.

      References:

      (1) B. M. Xavier, A. A. Zein, A. Venes, J. Wang, J. Y. Lee, Transmembrane Polar Relay Drives the Allosteric Regulation for ABCG5/G8 Sterol Transporter. International Journal of Molecular Sciences 2020, Vol. 21, Page 8747 21, 8747 (2020).

      (2) B. J. H. Johnson, J. Y. Lee, A. Pickert, I. L. Urbatsch, Bile acids stimulate ATP hydrolysis in the purified cholesterol transporter ABCG5/G8. Biochemistry 49, 3403–3411 (2010).

      (3) D. Farhat, F. Rezaei, M. Ristovski, Y. Yang, A. Stancescu, L. Dzimkova, S. Samnani, J. F. Couture, J. Y. Lee, Structural Analysis of Cholesterol Binding and Sterol Selectivity by ABCG5/G8. J. Mol. Biol. 434, 167795 (2022).

      (3) S. Thangapandian, K. Kapoor, E. Tajkhorshid, Probing cholesterol binding and translocation in P-glycoprotein. Biochimica et Biophysica Acta (BBA) - Biomembranes 1862, 183090 (2020).

    1. eLife Assessment

      This study presents an open-source reinforcement learning framework for the real-time, closed-loop optimization of spatiotemporal electrical stimulation in engineered neuronal networks. Using single-spike-resolution activity as continuous feedback, the authors provide solid evidence that their platform can identify stimulation patterns that drive specific activity motifs within a structurally constrained four-node circuit. While this reproducible system offers a valuable and accessible tool for interacting with biological neural networks in an adaptive manner, further validation is needed to determine how well these stimulation strategies generalize to larger, unstructured, or more conventional network architectures.

    2. Reviewer #1 (Public review):

      Summary:

      The manuscript presents several reinforcement learning (RL) approaches to control activity in small, ring-shaped neural networks. The paradigm is to find temporally and spatially patterned stimuli applied to axons that maximize the length of activity propagation along the network. Several RL versions were compared. Some RL-designed stimulation patterns worked better than others.

      Strengths:

      The work is technically and statistically very sound, and important controls were done that are missing in some earlier work along this line of research. The statistics and technical solutions seem solid, though I am no specialist in RL. The figures are well done and informative, with matching quality of the captions. The work presented is of value mainly for someone who wants to build a good closed-loop stimulation system to experiment with neuronal networks in-vitro.

      Weaknesses:

      The manuscript appears undecided about whether it wants to be about RL control, about a technical implementation of long-term stimulation in vitro, or about the properties of neuronal networks and interaction with them. The introduction is well written, comprehensive and insightful, focusing on biological aspects. Methods are then extensively about RL algorithms, without explaining why several were used or why these in particular, but with specialist language hard to understand for neuroscientists. The results then quantitatively compare the performance of the RL but do not really explain what this teaches us about neuroscience or what we learn about the networks beyond that they can be stimulated for longer propagation patterns. Extensive supplementary material almost advertises the hardware built by the team. Some figures suggest, though I'm not 100% certain about this, that different algorithms find different optimal stimulation patterns in the same network - which I find puzzling. What then does this tell us about the stimulation patterns and the variability of the responses? There is some speculative mechanistic reasoning, but no data to support this further. The conclusions hardly address the initial motivation of the manuscript. To me, it is not clear what can be learned that was not, in one way or another, presented previously, with as well as without RL.

    3. Reviewer #2 (Public review):

      The authors developed a reinforcement learning framework to identify stimulation patterns that produce a predefined activity pattern in neuronal networks cultured on a microelectrode array. The main contribution of this study lies in the development and validation of the experimental framework, rather than in providing new insight into neuronal response properties or the biological mechanisms underlying network activity.

      A major strength of the work is that the framework is implemented using open-source software. Electrical stimulation through microelectrode arrays is already widely used, and the proposed approach is therefore likely to be of interest to researchers seeking to automate the exploration and optimization of stimulation parameters. The authors also provide the software, hardware configuration, and experimental data, which substantially improves transparency and should facilitate reproduction and further development of the system by other laboratories.

      The experiments provide a useful proof of concept showing that the framework can search for stimulation patterns associated with the predefined task in the tested neuronal network. The evidence is solid for demonstrating the feasibility of the approach within this specific experimental configuration. In particular, the closed-loop integration of stimulation, recording, evaluation of the neuronal response, and subsequent selection of stimulation patterns is clearly implemented and experimentally tested.

      An important limitation is that the framework was evaluated using a relatively small and highly structured network. Four stimulation electrodes were positioned around a single network, and stimulation was delivered to microchannels in which axons were concentrated. This configuration is well suited to the initial demonstration, but it remains uncertain whether the same approach will perform similarly in conventional monolayer dissociated cultures, larger networks, or systems with different numbers and spatial arrangements of electrodes. Additional validation across a broader range of network structures and experimental configurations would therefore be needed to establish the general applicability of the framework.

      Overall, this study presents a valuable and reproducible methodological framework for the closed-loop optimization of electrical stimulation in cultured neuronal networks. Its likely impact lies primarily in providing an accessible experimental and computational platform that can be adapted for studies requiring systematic exploration of stimulation patterns, although the extent to which the current findings generalize beyond the tested network configuration remains to be determined.

    4. Reviewer #3 (Public review):

      Summary:

      This study integrates living neuronal circuits with a real-time reinforcement-learning framework to enable closed-loop optimization of directional firing sequences at single-spike resolution. By using the spatiotemporal structure of neuronal firing as feedback, the system adaptively searches for stimulation patterns that increase a predefined propagation reward. The work represents an exciting step toward systematic and adaptive control of living neuronal networks.

      Strengths:

      The study presents a cutting-edge closed-loop platform that combines neuronal cultures, high-temporal-resolution electrophysiology, and reinforcement learning with millisecond-scale latency. The ability to optimize directional activity propagation at single-spike resolution is particularly novel. More broadly, the work provides a compelling framework for interacting with biological neural networks in an adaptive rather than purely predefined manner.

      Weaknesses:

      Several aspects of the analysis and interpretation require further clarification. In particular, the definition and computation of directional propagation and reward are not always clear. The generalizability of the optimized stimulation patterns across cultures also remains unclear.

    5. Author response:

      eLife Assessment

      This study presents an open-source reinforcement learning framework for the real-time, closedloop optimization of spatiotemporal electrical stimulation in engineered neuronal networks. Using single-spike-resolution activity as continuous feedback, the authors provide solid evidence that their platform can identify stimulation patterns that drive specific activity motifs within a structurally constrained four-node circuit. While this reproducible system offers a valuable and accessible tool for interacting with biological neural networks in an adaptive manner, further validation is needed to determine how well these stimulation strategies generalize to larger, unstructured, or more conventional network architectures.

      We thank the editors and reviewers for their kind and insightful words, as well as their constructive feedback and assessment. We agree that in its current stage, the manuscript does not convincingly argue, that specific stimulation strategies applied to one network generalise well to other network architectures. We intend to address this problem by adjusting the focus of the manuscript to be more on the platform than on any specific neuroscience claim, as we believe that this is the more useful angle for the community at large. We also intend to discuss the transferability of our results between the presented culture system and other neuroscience systems.

      Based on the reviewer comments, we will further give a more approachable introduction to reinforcement learning to make the concept more accessible to a wider audience. We will also motivate the algorithm selection in more depth.

      In the following, we will discuss the comments put forward by the reviewers and how we intend to adapt our manuscript to address them. In this provisional response, we will only focus on major concerns. Minor comments, where we are following the suggestions made by the reviewers directly, will not yet be discussed.

      Reviewer #1 (Public Review):

      The manuscript appears undecided about whether it wants to be about RL control, about a technical implementation of long-term stimulation in vitro, or about the properties of neuronal networks and interaction with them. The introduction is well written, comprehensive and insightful, focusing on biological aspects. Methods are then extensively about RL algorithms, without explaining why several were used or why these in particular, but with specialist language hard to understand for neuroscientists. The results then quantitatively compare the performance of the RL but do not really explain what this teaches us about neuroscience or what we learn about the networks beyond that they can be stimulated for longer propagation patterns. Extensive supplementary material almost advertises the hardware built by the team. Some figures suggest, though I’m not 100% certain about this, that different algorithms find different optimal stimulation patterns in the same network - which I find puzzling. What then does this tell us about the stimulation patterns and the variability of the responses?

      We thank the reviewer for pointing out these issues with our manuscript. The goal of our work is the hardware framework. We plan to highlight this more in the revised version. The RL part will be revised with less specialist language and will get less emphasis in the next version of our manuscript. We will also discuss why different algorithms seem to find different solutions, which can be traced back to having different networks and agents finding different local maxima.

      By clearly setting the scope of our manuscript on the hardware aspects and revising the RL part to be seen more as one possible closed-loop control paradigm, we further intend to address the concerns put forward by the reviewer regarding our focus on RL in other parts of the manuscript.

      Reviewer #2 (Public Review):

      An important limitation is that the framework was evaluated using a relatively small and highly structured network. Four stimulation electrodes were positioned around a single network, and stimulation was delivered to microchannels in which axons were concentrated. This configuration is well suited to the initial demonstration, but it remains uncertain whether the same approach will perform similarly in conventional monolayer dissociated cultures, larger networks, or systems with different numbers and spatial arrangements of electrodes. Additional validation across a broader range of network structures and experimental configurations would therefore be needed to establish the general applicability of the framework.

      We thank the reviewer for their feedback. They are right to point out that the manuscript here focuses purely on small and highly structured networks. With the presented experiments, our manuscript does not and cannot make any biological claims about how neurons communicate. However, with this manuscript, we also do not intend to do so, as such an endeavour would be out of scope. In the next version of our manuscript, we will better highlight the focus of our manuscript, which lies on the hardware framework itself. Furthermore, we will discuss in the outlook the generalisability and scalability concerns raised by the reviewer in more detail.

      Reviewer #3 (Public Review):

      Several aspects of the analysis and interpretation require further clarification. In particular, the definition and computation of directional propagation and reward are not always clear. The generalizability of the optimized stimulation patterns across cultures also remains unclear.

      We thank the reviewer for their insightful feedback. We agree with them and will discuss (1) the currently implemented reward based on directional propagation of activity and (2) the limitations and aspects influencing reward algorithm selection in more detail in our revised version of the manuscript. We further believe that we cannot make any claims about generalisability between cultures or when changing the experimental paradigm.

    1. eLife Assessment

      In this valuable study, the authors build on previous work demonstrating that the microtubule-associated protein CKAP2 functions at the mitotic spindle to regulate microtubule dynamics for proper chromosome segregation. Here, the authors show that CKAP2 and its paralog CKAP2L also function at the cilium to regulate cilium formation and Hedgehog signaling. The writing is clear, the experiments and data are generally convincing, and the proper data analysis is carried out; however, the evidence supporting the proposal that CKAP2 and CKAP2L compensate for each other is incomplete. Overall, the findings advance the field in our knowledge of microtubule-associated proteins with roles in both the mitotic spindle and the cilium.

    2. Reviewer #1 (Public review):

      Summary:

      The authors examine the roles of CKAP2 and CKAP2-Like in ciliogenesis. They assess the localization of both proteins in primary and motile cilia of the human male reproductive tract and use hTERT-RPE1 cells to investigate CKAP2 function. They report that CKAP2 localization is associated with cell-cycle progression and that its intrinsically disordered region is required for ciliary localization. Loss of either CKAP2 or CKAP2-Like results in elongated cilia, whereas loss of both proteins reduces ciliogenesis and restores ciliary length to approximately wild-type levels. Based on these findings, the authors propose compensatory interactions between the two paralogs.

      Strengths:

      The study combines tissue localization, knockout cell lines, quantitative analysis of ciliogenesis and ciliary length, and live imaging of CKAP2 dynamics. The findings extend previous work implicating CKAP2 and CKAP2-Like in microtubule regulation and support roles for both proteins in ciliogenesis and cell-cycle-associated ciliary dynamics. Overall, the study is convincing and should be of interest to readers.

      Weaknesses:

      The proposed compensation model requires further support. Because cilia are longer in the single-knockout cells, the ciliary fluorescence signals of CKAP2 and CKAP2-Like should be normalized to ciliary length. The same issue applies to the acetylated-tubulin fluorescence measurements. Furthermore, the ciliary-length phenotypes and increased localization of the remaining paralog do not by themselves establish functional compensation. Additional experiments are needed to determine whether acute depletion of the remaining paralog in each single-knockout background reproduces the double-knockout phenotype and whether expression of either paralog can rescue the double-knockout cells.

    3. Reviewer #2 (Public review):

      Summary:

      The authors build on previous work demonstrating that the microtubule-associated protein CKAP2 functions at the mitotic spindle to regulate microtubule dynamics for proper chromosome segregation. Here they show that CKAP2 and its paralog CKAP2L also function at the cilium to regulate cilium formation and Hedgehog signaling.

      Strengths:

      A major strength of the manuscript is the use of GFP-CKAP2 knockin cells and the examination of single and double CKAP2 and CKAP2L knockout cells. The authors use live imaging of the knockin cells to show that CKAP2 localizes to the primary cilium of RPE1 cells after assembly.

      The authors use the knockout cells to show that loss of either CKAP2 or CKAP2L results in longer cilia and a dampened Hedgehog response, whereas loss of both CKAP2 and CKAP2L results in shorter cilia and an even more dampened Hedgehog response.

      Weaknesses:

      The data are compelling and support the conclusions. However, the findings would be significantly strengthened by using additional markers of the cilium, not just acetylated tubulin.

    1. eLife Assessment

      This valuable study shows first that yeast strains potentially associated with the [MRPL10+] prion state strongly influence Drosophila cold tolerance and locomotor behavior, and it goes on to exploit natural variation to identify host pathways contributing to these responses. The evidence for host physiology and genetics is solid, but the evidence for MRPL10 prion causality is incomplete because the prion state has not been directly validated in the yeast strains used. The study would be substantially strengthened either by establishing the presence of the MRPL10 prion state, or by tempering the prion-specific causal claims and framing involvement of MPRL10 or other prions in the yeast strains as a plausible but unproven mechanism. The work will be of interest to many fields, including quantitative genetics, insect physiology and microbe-host interactions.

    2. Reviewer #1 (Public review):

      This manuscript by Lin et al. was a pleasure to read, and the results were quite surprising. The first surprise was the strength of the effects of the [MRPL10+] yeast prion on Drosophila locomotory behavior and chill recovery. The second was that this effect is highly polymorphic in different Drosophila lines, allowing the authors to perform a GWAS and implicate a number of genes involved in gut-brain signaling, thereby establishing a clear mechanism for this effect. The third was that many of these GWAS hits were validated by RNAi, giving further support to their involvement. I only have a few minor questions.

      (1) Does the evidence really "support a model in which [MRPL10+] yeast promotes cold tolerance by activating serotonin signaling while simultaneously suppressing octopaminergic signaling"? I agree about serotonin, but the role of octopamine seems less clear. Figure 3d shows that mutants for genes involved in octopamine synthesis (Tdh2) and a receptor (Octβ2R) have higher cold tolerance, irrespective of [MRPL10+]. So while this does establish a role for these two genes in cold tolerance, what is the evidence that [MRPL10+] suppresses octopaminergic signaling? These seem to be two different questions. In other words, maybe any mutant with increased cold tolerance would show no response to [MRPL10+], regardless of whether the mutated gene is involved in mediating [MRPL10+]'s effects.

      (2) I was also somewhat confused by the results of Supplementary Figure 4. In S4a, inhibition of octopaminergic neurons (TDC2 > TNT) seems to decrease locomotory behavior in response to [MRPL10+], which is the opposite of what I would expect from the cold tolerance experiments in Figure 3d. And then in the S4c legend, it is stated that "Loss of the octopamine biosynthesis gene Tdc2 or its receptors Octβ2R and Octβ3R triggers elevated locomotor activity even in flies fed wild-type yeast, mimicking the [MRPL10+] phenotype." I can see this for the two receptors, where both the red and black lines show high locomotor activity (>1000 passes) near the middle of each 24-hour experiment, but the Tdc2 graph in S4c looks quite different, with the [MRPL10+] activity never going above ~200 passes at any time point and the wt activity showing high variability. So if I understand the data correctly, it seems that Tdc2 mutants have higher cold tolerance (Figure 3d) but not higher locomotory behavior (Figure S4c).

      (3) To summarize, I don't see how the data in either Figure 3d or Supplementary Figure 4 support a role of octopamine in the [MRPL10+] mechanism. Even in cases where perturbing a gene/neuron type abolishes the effects of [MRPL10+] on locomotory behavior or cold tolerance, this establishes a role for them in locomotory behavior or cold tolerance, but not for mediating [MRPL10+]'s effects (for the same reason given in #1 above). In contrast, the 5-HT2A results in Figure 3d do establish mechanistic relevance for serotonin, since the mutant affects only the response to [MRPL10+], but not wt.

      (4) Finally, I am intrigued by the idea at the end of the paper that this represents a symbiotic adaptation. Have [MRPL10+] yeast ever been found in the wild? If not, perhaps its effects on flies are unlikely to represent anything adaptive? And if the prion is present in the wild, I wonder: if it's to a fly's advantage to have higher cold tolerance or locomotory behavior, why couldn't it evolve that itself, without needing a prion to induce it? Given the incredible adaptability of flies, I would guess that any physiological effects of a yeast prion would more likely push a fly away from its fitness optimum, and only benefit the yeast-- for example, by increasing yeast dispersal at the cost of a fly's fecundity. The main exception to that would be if [MRPL10+] yeast were only present in some specific environments that the flies couldn't adapt to on their own, but that seems unlikely.

    3. Reviewer #2 (Public review):

      Summary:

      In this manuscript, the authors report on how a prion protein originating in yeast inside the gut interacts with a host insect's physiology to influence organismal traits important to fitness. Other recent studies have strongly found that interactions between gut yeasts and their Drosophila hosts are important to feeding behaviour, growth performance, and cold tolerance. They even point to the gut-brain axis. The authors extended from this work, combining screening approaches, genome-wide association studies, and forward genetics to determine that octopamine and serotonin signalling are important to these effects.

      Strengths:

      The data represent a valuable addition to the literature. There is a growing appreciation that host-microbiome interactions are important to animal performance and fitness, and this work provides strong evidence for a causal role of a prion protein in such a relationship. The high-throughput screening approaches and sequencing were, to the best of my knowledge, completed correctly. The idea that prion proteins could influence host physiology in beneficial ways is compelling, and the authors have done a great job of walking the reader through their reasoning and the multiple lines of evidence that support their reasoning.

      Weaknesses:

      While this is an interesting topic that is worthy of investigation, and identifying a causal role for a prion protein in host effects is a significant advance, other aspects of the novelty of the work are strongly overstated. No clear justification is provided for why cold tolerance was a particular trait of interest in the present study. Yet, a recent publication in the Journal of Experimental Biology clearly demonstrated a role of gut yeasts in Drosophila cold tolerance and is not cited or otherwise acknowledged [see: Jiménez-Padilla and Sinclair. Gut yeasts accelerate chill coma recovery in Drosophila melanogaster. J Exp Biol (2026) 229 (9): jeb251533]. I trust this is an oversight and will be corrected. Integrating this knowledge into the narrative of the paper would strongly improve the reasoning for this study.

      The authors rely on a review of chill coma mechanisms from 2011 as their primary source of information for interpreting their results in relation to insect cold tolerance. A great deal of work has been done by several research groups on the physiology of cold tolerance in chill-susceptible insects in the past 15 years, much of which has relied on Drosophila as a model system. I think this is important and could inform the conclusions being reached and future work that extends from this work. For example, it is now well established that the renal system is centrally important to insect cold tolerance, including chill coma recovery phenotypes, but this is not being acknowledged or integrated into the narrative here and should be. Many of the mechanisms of interest highlighted in the present work are also directly relevant to other ionoregulatory epithelia beyond nervous tissue, and global knockdowns using the UAS/Gal4 approach will impact all tissues in which these genes are expressed. Figure 2D shows how these genes are enriched in the CNS, but also the midgut and Malpighian tubules, two ionoregulatory organs of import to cold tolerance.

      The method the authors used to assess cold tolerance is not typically preferred by researchers who commonly study these phenotypes. Holding flies continuously at 4-6{degree sign}C and assessing whether or not they remain active is a reasonable approach, and I am confident that the differences observed among groups are real. However, chill coma onset and recovery are thought to be mediated by a complex combination of temperature effects on nervous, muscle, and renal function, and this assay method may conflate chill coma resistance with the capacity for chill coma recovery. This means variation in this assay could arise from differences in initial CTmin, plasticity in CTmin induced by RCH before chill coma is induced, or plasticity in neurumuscular/renal function that allows for recovery during the assay. I say this in part because Drosophila are documented to enter chill coma and then recover while the assay is ongoing, particularly around temperatures close to the CTmin [see: MacMillan et al., The capacity to maintain ion and water homeostasis underlies interspecific variation in Drosophila cold tolerance. Sci. Rep. 5:18607]. Therefore, flies active after 2 hours may never have entered chill coma, or may have entered and then recovered. Serotonin and octopamine signalling may be critical to one or both of these possibilities, and maybe in different ways, especially because these hormones play critically important roles in both renal and nervous function.

      Infectious prions are known to cross gut epithelia and the Drosophila gut is known to take up large organic molecules via endocytosis and exocytosis, so I am not entirely convinced that the effects observed necessarily result through the gut-brain axis. Future studies could be focused on observing whether these prion proteins remain in the gut, or act directly on other tissues via the hemolymph.

      The CAFÉ assay used in the study is a tried and true way to measure feeding behaviour in Drosophila. The data in Figure 1H look like a single measurement per individual, unless they have been modified for presentation. From the text provided, it is not clear to me how the authors accounted for changes in feeding behaviour at different times of day. Feeding behaviour is regulated by the circadian rhythm in digestive tissues. If activity is enhanced at night, as in the present study, are MRPL10+ flies also feeding during the night more than the WT flies?

    4. Reviewer #3 (Public review):

      Summary:

      This manuscript shows how a change in food source can lead to organismal adaptation to stress. Namely, different yeast strains impact the response of Drosophila to cold stress. An intriguing connection between food intake, gut, and neuronal signaling is described.

      Strengths:

      The genetic association studies suggest how pathways can be altered to provide cold tolerance in Drosophila.

      Weaknesses:

      [MRPL10+] is described here as a yeast prion, yet there is no solid evidence supporting this. It is unclear whether the prion exists; therefore, the primary conclusion is unclear. More biochemical analyses and additional characterization are needed to support the idea that MRPL10 is, in fact, in a prion state in the strains the authors received and utilized in this manuscript. It is unclear whether the mating scheme to produce daughter cells without the prion would actually work as described, and no follow-up work verifies the lack of the prion state. In addition, more common prions were omitted from the study, and it is unclear whether they might be present in the strains used as well. There is no [PSI+], [RNQ+], or [URE3] strain assessed, and it is possible that there is another prion in the [MRPL10+] strain.

    1. eLife Assessment

      This valuable study presents a comparative framework for quantifying adaptation in SARS-CoV-2 and influenza H3N2 from changes in clade frequencies, enabling comparison of rates of adaptation across the two viruses on a common per-generation scale and showing that a simple count of mutations performs as well as much more sophisticated protein language models in predicting variant fitness. The evidence supporting the quantitative estimates of fitness flux is currently incomplete because the analysis assumes a single, time-independent fitness for each clade; for antigenically evolving viruses, clade fitness changes with population immunity, and it remains unclear how this assumption affects the estimated fitness flux. The interpretation of cumulative fitness flux in relation to changes in absolute fitness over time also requires clarification.

    2. Reviewer #1 (Public review):

      Summary:

      This manuscript calculates a measure of fitness flux for influenza and SARS-CoV-2 viruses, normalising by a generation time go generate comparable measures of viral adaptation over time. Potential correlates with gain in fitness flux, such as the number of mutations in different regions of the virus, are evaluated in terms of their correlation with the measured gain. A count of mutations in the S1 subunit of Spike performs slightly better in explaining observed fitness changes than previous deep-learning models describing SARS-CoV-2 evolution.

      Strengths:

      I enjoyed reading this manuscript. I liked the characterisation of fitness effects in a way that makes influenza and SARS-CoV-2 directly comparable. This is a neat new result.

      I thought the retrospective regression against numbers of variants was a simple and interesting analysis. There is some precedent for examining correlates of fitness in key viral sites, for example in Bush et al., 1999 and Luksza and Lassig 2014: This carried on a useful tradition.

      This paper is clearly written and well-constructed. The figures are well produced. There is an absence of hype in presenting results in a simple and straightforward manner.

      After posing the question, I came to the conclusion that the comparison between S1 mutations and EVEscape was a fair one. The question here is that the details of the relationship between S1 mutations and fitness is derived in retrospect, from a large quantity of data, whereas PLM-based scores are advertised as having utility on the basis of only a single genome sequence. However, comparison was made with a fine-tuned model, which in some ways levels the playing field. Maybe further discussion could have been added around this point?

      Weaknesses:

      (1) I wasn't clear about whether the comparison with Fisher's fundamental theorem was a useful check on the analysis, or simply an inevitable consequence of any estimate of fitness scores. The fitness of a strain is measured in relative terms, as the difference in a fitness score relative to the mean of that score for the whole population. More rapid adaptation occurs when the relative difference to the mean is large, which may necessarily correlate with an increased overall variance in the fitness of strains.

      (2) I would have valued more discussion on the potential implications of the findings of this manuscript. Two questions I was left with:

      a) Thinking about application to a potential future emergent pathogen, to what extent is prior knowledge being leveraged in these methods? While a comparison is made of fitness gain versus statistics of multiple genes, in one sense we 'know' that the more accessible parts of the viral glycoprotein are going to be of higher relevance for viral fitness and evolution, simply from an understanding of the necessary geometry of antibody-viral interactions. Similar knowledge is leveraged in the Luksza and Lassig 2014 study: Characterising fitness by a number of epitope substitutions in that case rests on biological knowledge of which are the important epitope sites. The EVE family of methods use a similar approach where factors such as residue accessibility are combined with outputs from protein language models. But does this work have implications for how we evaluate emerging variants of existing viruses, notably SARS-CoV-2 or influenza? The H3N2/K substrain of influenza makes me want to ask this question: In that case a large number of non-synonymous changes in HA was not accompanied by a noticeable increase in the severity of seasonal influenza.

      (b) What is the implication of this work for the use of PLM-based models for understanding virus evolution? One analysis that was not done is evaluating the performance of combined models, encompassing both mutation counts and PLM data. Models such as EVEscape already utilise multiple metrics alongside PLMs. Do PLMs contribute addition knowledge to a 'number of mutations on the outside of the glycoprotein' count?

    3. Reviewer #2 (Public review):

      Summary:

      The author uses multinomial logistic regression to assign fitness values to SARS-CoV-2 and Influenza H3N2 clades. The mean population fitness, referred to as the cumulative fitness flux, is tracked throughout evolution to describe the rate of adaptation. The author shows a clear decrease in the rate of adaptation for SARS-CoV-2 after mid 2022, coinciding with the emergence of the Omicron clades; the rate of adaptation of H3N2 is much slower, but inferred to be constant in time. With this analysis, the author confirms Fisher's fundamental theorem of natural selection. Using a fine-grained clade definition scheme, the author attributes changes in fitness to substitutions in different genomic regions, highlighting the importance of the spike and, in particular, the RBD region of spike. A fitness model based on a multinomial regression is shown to perform better than EvEscape and protein-language-model semanticity to predict changes in fitness.

      Strengths:

      The author provides an interactive reading experience on the github.io hosted website, which has additional interactivity with figures and offers simple manipulation of what is shown. The methods are well explained and are easy to follow. The conclusions of the study- the decrease in the rate of adaptation of SARS-CoV-2, mutations in Spike S1 as the main driver of evolution, and the Spike S1 mutational count as predictor of fitness change - are clear to follow.

      Weaknesses:

      (1) The main conclusions that (i) the rate of adaptation of SARS-CoV-2 has decreased after Jun 2022 and is lower than in Influenza H3N2 and (ii) that mutations in Spike S1 are the main drivers of evolution are not novel.

      (2) At the core, the multinomial logistic regression assumes a time-independent fitness value for each viral clade, while the clade fitness is not constant and depends on the population immunity, which will change over time. As a result, the fitness increase can be wrongly interpreted: The author observes a doubling of the mean fitness every 6 months from Jan 2021 to Jun 2022, and every 2.4 years from July 2022 to Dec 2025. We would then expect that the mean fitness in Dec 2025, \bar{f}_t, has increased more than 2 to the power 4, as compared to the mean fitness in Jan 2021, \bar{f}_0 (\bar{f}_t > 2^{4} \bar{f}_0). Is the mean viral fitness in Dec 2025 more than 16 times higher than in Jan 2021? Fitness differences between viral populations at different times cannot be meaningfully compared because the fitness landscape is time-dependent. The increase in fitness, without accounting for the change in population immunity, is in part an artifact of the multinomial regression.

      (3) The multinomial regression treats the fitness landscape on which the virus evolves as static. However the fitness landscape is actually time-dependent, as population immunity changes. For a static fitness landscape, the cumulative fitness flux is the mean population fitness. However, for a dynamic fitness landscape, this is no longer the case: the (mean population) fitness changes both because of adaptation, and because changes of changes in the fitness landscape, i.e., an increase in population immunity will decrease fitness non-uniformly, also affecting individual selection coefficients s_i(t). This leads to cases where the cumulative fitness flux between t_0 and t_n can be positive, but the fitness remains the same. This study neglects the time-dependence of the fitness landscape and therefore incorrectly calculate the fitness flux, leading to an overestimation of the rate of adaptation.

    4. Reviewer #3 (Public review):

      Summary:

      Bedford considers how the fitness of SARS-CoV-2 and influenza H3N2 variants have changed over time. The fitness of viral variants are estimated from trajectories of variant frequencies using multinomial logistic regression (MLR). The MLR approach was previously developed and is well-validated. Changes in mean population fitness (i.e. fitness flux) is then derived from changes in the fitness of circulating variants through time.

      Strengths:

      The manuscript makes some noteworthy observations. For one, it is reassuring to know that fitness variation well predicts the rate of fitness change in viral populations, as expected under Fisher's fundamental theorem. Similarly, it is interesting that simple counts of mutations perform as well as much more sophisticated protein language models in predicting variant fitness.

      Weaknesses:

      My two main concerns both relate to the interpretation of fitness flux and how confident we should be that it is quantifying changes in absolute fitness as opposed to environmentally-dependent changes in fitness through time.

      (1) Interpretation of fitness flux:

      I'm not entirely convinced that mean population fitness can be meaningfully compared through time, at least not as a measure of fitness change on an absolute scale. Here, mean population fitness is defined as the weighted sum of the relative fitness of each variant, so changes in mean pop fitness necessarily reflect changes in relative fitness. To obtain a fitness value of each variant that is comparable across time, a "scaffolding approach" is used to estimate window-specific effect and the global fitness (f_i) of each variant. The major assumption here is that by accounting for the window-specific effects, the global fitness of each variant becomes directly comparable. This assumption seems somewhat suspect. It seems impossible to know whether the window-specific effect is capturing the full environmentally-dependent component of fitness in a given window. There could also be a high degree of temporal autocorrelation in window-specific effects between neighboring windows due to similarities in environment that would decay over time.

      To put this in another way, say we directly competed a 2020 SARS-CoV-2 variant with a 2025 variant in the distant future after all host-immunity to these strains had waned. Would we really be confident in estimating the fitness advantage of the 2025 variant over the 2020 variant? Or would this estimates of relative strain fitness still reflect the environments in which they were measured in, including the NPIs that were in effect in 2020 and the complex immune structure of the host population in 2025?

      (2) Inability to separate different components of fitness:

      Earlier work, like Meijers et al. for SARS-Cov-2 (Cell, 2023), decomposed overall changes in fitness into different components of fitness such as changes in intrinsic fitness (transmission potential) and immune escape. While the current model is simpler, in some ways this earlier work was more elegant and more powerful in its ability to explain what components of fitness drove the overall change in population-level fitness.

    1. eLife Assessment

      This important study investigates how retinoic acid signaling and retinal support cells called Müller glia might contribute to regional differences in the morphology and function of cone photoreceptors - light-sensitive neurons in the zebrafish retina - and may have broader implications for understanding how non-neuronal cells such as glia shape neuronal cell specialization in other regions of the nervous system. Despite well-executed imaging and pharmacological experiments, the evidence remains incomplete because the proposed mechanism is not established as specific to Müller glia, and broader effects on retinal development, cell growth, maturation, and/or differentiation could explain the observed shortening of light-sensitive structures. The work will interest researchers studying vision, nervous system development, and interactions between neurons and their supporting cells.

    2. Reviewer #1 (Public review):

      Summary:

      The authors examine the role of retinoic acid signaling in the formation of lengthened outer segments in UV cones in a localized region within the ventral temporal zebrafish retina. The authors demonstrate that the formation of this region of increased sensitivity depends on the degradation of retinoic acid by Cyp26, which is expressed in MG. Inhibiting Cyp26 or stimulating RA expression reduces OS length and visual sensitivity.

      Strengths:

      A key strength is the use of several experimental strategies to support conclusions. For example, the authors localize key enzymes in Müller glia (MG) and, in a separate experiment, to highlight the overall importance of MG, they ablate them. High acuity zones are critical visual specializations, and understanding their formation is important for vision research, retinal disease, and regenerative medicine. The manuscript also addresses a fundamental question in neuroscience: What are the specific signaling mechanisms that occur between neurons and glia during development?

      Weaknesses:

      The primary weakness is the use of inhibitors, which have systemic effects. This caveat has been appropriately addressed in the discussion.

    3. Reviewer #2 (Public review):

      Summary:

      This manuscript by Lahne et al. connects retinoic acid (RA) signaling regulation to the length of outer segments (OS) in UV-sensitive cone photoreceptors located in a specialized region of the zebrafish retina called the high acuity zone (AZ). The authors provide potentially compelling, but as it stands, incomplete, evidence that the Müller glia (possibly through expression of the Cyp26 RA-degrading enzyme) may act to regulate spatially restricted RA availability to UV cones in the AZ, supporting these UV cones to grow longer OS. While the evidence presented is indeed suggestive, it is not directly linked and is insufficient to support the full conclusions presented.

      Strengths:

      (1) The experiments make outstanding use of the strengths offered by the zebrafish model, including excellent reporter lines for RA signaling and Müller glia visualization, fluorescent in situ hybridization, high resolution fluorescent microscopy, and pharmacological manipulations; tools which are all well selected and utilized.

      (2) The pharmacological manipulations allow temporally controlled modulation of RA signaling and Cyp26 enzyme activity at developmental timepoints of interest.

      (3) Manipulation of RA signaling impacts cone OS lengths.

      (4) The Müller glia express mRNA transcripts encoding the RA-degrading enzyme Cyp26 and respond to RA signaling modulation in a manner consistent with a role in regulating RA signaling.

      (5) The use of DAPT to prevent Müller glia differentiation in the retina is used to attempt to implicate the Muller glia in the regulation of RA signaling and UV cone OS length.

      Weaknesses:

      (1) The experiments cannot rule out a role for other cell types in driving the outcomes studied (UV cone OS length). The expression of Cyp26 transcripts by the Müller glia does not specifically align with the high acuity zone, and other cell types show signal for these transcripts. Impacts on RA signaling, as well as Cyp26 expression can be detected in regions potentially implicating cell types other than the Müller glia. One region of Cyp26 transcript signal is consistent with the Retinal Pigment Epithelium (RPE), and cells in this same region also show RA signaling changes in response to pharmacological treatments.

      (2) The analysis following pharmacological manipulations lacks quantification of UV cone numbers and their morphology after the treatments, which could indicate broader impacts on cones that may manifest in their outer segments.

      (3) The cell-type assignment of RARE:GFP-positive ONL cells is central to the proposed non-cell-autonomous mechanism, yet representative colocalization images supporting the quantification are not fully shown.

      (4) The SyGCaMP6f activity reporter experiments are a nice addition, but the results are not necessarily directly connected to OS length or to the Müller glia.

      (5) The experiments using DAPT to prevent Muller glia development are not sufficiently executed to support the conclusions presented, and their efficacy in inhibiting Müller glia development, as well as UV-cone-specific impacts, are not demonstrated. These treatments could have broad impacts on photoreceptor maturation, retinal organization, or trophic support.

      (6) The title refers to glial regulation of "local retinoic acid levels." However, the study measures: A synthetic RARE reporter, RA-responsive genes and pharmacological responses.

    4. Reviewer #3 (Public review):

      Summary:

      The manuscript titled "Glia regulate local retinoic acid levels to specify neuronal specialisation for high-acuity vision" by Lahne et al. examines the interaction of cellular and signaling mechanisms involved in neuronal specialization. While there have been rapid increases in the discovery and cataloguing of different cell types based on transcriptional signatures that can be uncovered by single-cell approaches, there is much less known about how variations in cell morphology within a cell type are governed. The highly stereotypical specializations in photoreceptor morphology found in high acuity zones of various species are an excellent example. This study implicates retinoic acid signaling as a functional driver of these photoreceptor specializations in addition to its previously described role in establishing the spatial positioning of high acuity zones within the neural tissue. Furthermore, evidence is presented that retinoic acid does not directly signal through photoreceptors, but instead this signal is received by Müller glia to non-cell-autonomously affect the photoreceptors. Overall, this study identifies a potential mechanism that integrates the broad patterning role of retinoic acid with its local effects on specializations within a cell type and suggests that glial cells are integral to this mechanism.

      Strengths:

      The major strengths of this study are the well-executed experiments and clearly communicated results. The application of pharmacological agents is useful for precise timing of target inhibition and to prevent effects on other retinoic acid-mediated processes.

      Weaknesses:

      There are two issues with the study that weaken the conclusions that are drawn. The first is that the primary evidence that Mueller glia are the cells receiving the retinoic acid signal is that the RARE reporter is active in these cells. However, it should be considered whether this reporter is capable of responding to all retinoic acid signaling. Is it possible that the construct's binding site sequences and/or their configuration will limit the ability to detect other receptor complexes that respond to retinoic acid? For example, will complexes that contain RorBeta or RxrGamma (two established retinoic acid receptors) in complex with thyroid receptors be able to activate this reporter? If that is the case, then it could be that the Mueller glia are shaping the retinoic acid availability in the local environment of cones through their expression of the Cyp26 genes, but the cones as well as the Müller glia are directly responding to the retinoic acid signaling. If there are methods to test this, such as directly activating/repressing retinoic acid signaling in cones or MG with some cell type specificity, that would greatly strengthen the argument that it is a non-cell autonomous effect. One experiment that does argue in favor of this is that the loss of MG leads to a small outer segment, but this is where the second issue should be considered. The study uses three methods (including genetic loss of MG) that block the acuity zone-specific elongation of the photoreceptors, but one concern is that the shortening of these outer segments is a non-specific effect. In all three of the treatments (Figures 3, 5, 9), the segments look somewhat disorganized and not simply like a conversion of the Acuity zone outer segments to the Dorsal state. It would be powerful to complement these approaches with a retinoic acid-related manipulation that does the opposite - that converts Dorsal outer segments into a longer Acuity zone-like morphology. That would be strong support for the proposed hypothesis.

    1. eLife Assessment

      This valuable study examines excitation/inhibition (E/I) balance in the CA3-CA1 circuit of the hippocampus, yielding results that suggest a way in which CA1 neurons can perform mismatch detection. A combination of experimental and computational modeling results are presented and supported by solid evidence. The results are likely to be of interest to researchers who study computations in the hippocampal circuit.

    2. Reviewer #1 (Public review):

      Summary:

      This study uses optogenetics to activate CA3 while recordings from CA1 neurons and characterizing the excitation/inhibition (E/I) balance. They observe use-dependent alterations in the E/I balance as a result of STP and they develop a model to describe these observations. This is a very ambitious paper that deals with many issues using both experimental and modeling approaches.

      Strengths:

      This paper examines important principles regarding the manner in which synaptic circuitry and use-dependent synaptic plasticity can transform inputs and perform computations.

      Weaknesses:

      (1) I am pleased that the authors revised their section on external calcium They now approach this issue by calculating the effect of low external calcium on plasticity. These calculations are based on studies of numerous types of synapses, which are unlikely to be directly comparable to hippocampal synapses. They focus on PPR rather than the overall response during more sustained activation that is likely to produce more complex responses. Obviously, it would have been preferable to simply measure the responses in the appropriate external calcium.

      (2) The illumination intensity and the effect on fiber stimulation is a limitation that will alter apparent plasticity.

    3. Reviewer #3 (Public review):

      Summary:

      This work shows experimentally and computationally that single CA1 neurons can perform mismatch detection on patterned CA3 inputs and that STP and EI balance underlie this detection.

      Strengths:

      It has been known that STP can enhance the EPSP when the corresponding presynaptic input exhibits abrupt changes in firing rate. This work provides experimental evidence and further computational support for the hypothesis that the basic computation through STP is useful for detecting abrupt changes in the spatial pattern of synaptic inputs at the Schaffer collaterals. Further, their results indicate the novel view that mismatch detection is most efficient when gamma-frequency bursting inputs exhibit mismatches between theta cycles. The authors included novel results in the revised manuscript to show that the effective frequency range of gamma oscillation is broad, including both slow and fast gamma bands.

      In the initial submission, the dependence of mismatch detection performance on model parameters and experimental settings, such as pattern overlaps and other network parameters, was not sufficiently explored. In the revised manuscript, the authors extensively studied these points and summarized the novel results in Fig. 9. Furthermore, the authors clarified that jitters in input spikes can improve detection performance in some cases. These results show the robustness of their results against variations in external and internal conditions.

      Weaknesses:

      While this study shows an intriguing example of combined experimental and computational studies, the results of experiments and those of the computational model display relatively large deviations in some conditions. The cause of this discrepancy needs further clarification.

    4. Author response:

      The following is the authors’ response to the previous reviews.

      In this revision we have addressed all the reviewer comments, most particularly we acknowledge that our calculations and understanding of calcium levels were incorrect, and have revisited all sections of the paper affected by this. We have performed numerous additional analyses on data, rerun many calculations with low-Calcium versions of models, and have restructured several figures and text. In brief, here is an overview of the changes in this revision:

      Changes regarding Calcium levels.

      (1) We have referred to the literature for short-term plasticity effects over a range of concentrations for bath calcium, both in glutamatergic and GABAergic synapses in the hippocampus. By interpolation, the increase of PPR when going from 2 to 1.35 mM is below ~20%.

      (2) We used 1.35 mM external Calcium in revised presynaptic neurotransmitter release models, and show that both glu and GABA models follow the same direction of change as seen in experiments at lower Calcium.

      (3) We have rerun the key calculations using these updated synaptic models and show that mismatch detection remains intact, but its strength is diminished, at 1.35 mM calcium.

      Changes regarding use-dependent decrease in CA3 field responses:

      (4) We have performed additional analysis on recordings to show that the decline in CA3 field response over the 32 pulses of the mismatch trials is under 30%, and that the mismatch responses are present even when this decline has saturated.

      (5) Additional simulations without ChR2 desensitization show no effect on mismatch selectivity.

      Changes regarding analysis of jitter:

      (6) We have performed extensive additional simulations to better characterize and analyze the effects of jitter on mismatch selectivity.

      Changes regarding reliability of spiking:

      (7) We have added a section referring to our previous work using a similar protocol (Bhatia et al., eLife 2019), where we see a range of spiking probabilities from recorded CA3 neurons. We also refer specifically to our previous analysis which set tight limits to the number of spikes per light pulse. We present our interpretation of how this affects mismatch detection.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This study uses optogenetics to activate CA3 while recordings from CA1 neurons and characterizing the excitation/inhibition (E/I) balance. They observe use-dependent alterations in the E/I balance as a result of STP and they develop a model to describe these observations. This is a very ambitious paper that deals with many issues using both experimental and modeling approaches.

      Strengths:

      This paper examines important principles regarding the manner in which synaptic circuitry and use-dependent synaptic plasticity can transform inputs and perform computations.

      Weaknesses:

      There are three issues that cause concern regarding the applicability of their slice recordings to physiological conditions and that make some aspects of their results difficult to interpret. First, they state that 2 mM added external calcium mimics calcium levels in CSF, but this is not the case. This will influence the plasticity they observe.

      We agree that our calculations and understanding of calcium levels were incorrect. We have addressed this issue by referring to the literature in which external Ca is varied while measuring PPR.. From experiments by Blatow et al and Kravachenko et al we estimate that the PPR is ~20% higher at 1.35 mM external Ca than at 2mM, both for Glu and GABA synapses, but note that their experiments used a different set of conditions than ours. Based on this, we redid our synaptic plasticity models for Glu and GABA, reran our calculations and confirmed our core prediction of mismatch detection. We have added the following text to the introduction:

      “While most studies on synaptic plasticity utilize 2mM external Ca2+ (extCa) this is substantially higher than physiological Ca2+ (1.2 to 1.5 mM) and has been shown to significantly alter plasticity outcomes. Long-term plasticity is particularly sensitive to elevated extCa levels (Inglebert et al. 2020). There are significant but smaller changes in short-term plasticity in several systems, wherein lower extCa leads to a) higher paired pulse ratio (PPR) and b) lower EPSCs (Rat hippocampal culture: (Sippy et al. 2003), Calyx of Held: (Thanawala and Regehr 2013; Bernal-Correa et al. 2026; Lorteije et al. 2009), Mouse hippocampal slice: (Blatow et al. 2003)). Specifically, in hippocampal CA3->CA1 glutamatergic synapses at 1.5 mM extCa the paired-pulse ratio is 15% larger than at 2mM (Blatow et al. 2003). In primary hippocampal neuronal culture, inhibitory synapses also undergo a 50% increase in paired-pulse ratio at 0.5 mM compared to 2 mM extCa (Kravchenko et al. 2006). By linear extrapolation/interpolation both these results suggest a PPR increase of ~20% at 1.35 mM extCa compared to the conventional 2 mM. In the current study we extend our simulations into the physiological domain of extCa to check consistency of the predicted computational outcomes.”

      Also, from the methods:

      “Model tuning for physiological conditions

      We implemented two sets of changes to build a distinct presynaptic model which more closely matched physiological conditions. First, we increased the rates of all the reaction steps in the E and I presynaptic model by 40%, with the assumption of a 3.5℃ rise in temperature and a doubling of rates every 10℃ (Q10 of 2). Second, we lowered the calcium influx during stimulus by a factor of extCa/defaultCa = 1.35mM/2.0mM, again both for E and I. This effectively halves synaptic release (Figure 4-figure supplement 8, compare panels A and B), which we compensated for by a simple doubling of synaptic weight, both for E and I.

      It also causes an increase in STP levels compared to the reference 2.0 mM extCa model. We confirmed that these changes were qualitatively similar to those expected from experiments over a range of extCa levels (Kravchenko et al. 2006; Blatow et al. 2003). However, several features in these earlier experiments differed from ours: the inter-pulse intervals, the preparation, and the temperature. Accordingly, rather than paste in their numbers of ~20% increase in STP in E and I, we used the 37℃ and 1.35mM extCa variant of our model for additional simulations.”

      Second, they indicate that there is a 2% decrease in activated fibers per stimulus and attribute this to ChR2 desensitization. Such use-dependent decreases in fiber activation are expected to build during their repetitive activation experiments and artifactually influence their results.

      The reviewer correctly points out that there would be a substantial decrease in fiber activation if the 2% decrease per pulse extended linearly over the entire train of ~200 pulses in the Poisson train or 32 pulses in the mismatch experiments/models. Clearly this does not happen, as the EPSPs settle rapidly to a steady range in both cases (Figure 6A, E, Figure 7 E). We acknowledge that the original statement implied a large decrease, and have added the clarificatory line around Figure 1:

      “We attribute this to ChR2 desensitization, however in longer pulse trains this levels out as we discuss below.”

      To quantitatively address the concern about decline of activation, we have performed an additional analysis to estimate how fiber activation evolves over multiple pulses. For the Poisson train experiments, we used our measured field potentials over the entire train (Figure 6-figure supplement 2) and saw that the level flattened out after ~2 seconds (~40 pulses). For the mismatch experiments, the mean fEPSP flattens with a time course of ~0.8 seconds and even at 50Hz the decrement is only about 30% of the initial value (Figure 7-Figure Supplement 2). Thus the field readouts show that the saturating level of use-dependent decrease is small. From the text around Figure 7:

      “We observed this mismatch selectivity despite the reduction in inputs due to ChR2 desensitization over the long 32-pulse train. To estimate this reduction, we measured the field EPSP in the CA3 during the optical stimulation, and fitted an exponential decay function to the peaks (Figure 7-figure supplement 2). We find that the largest decay was ~30% of the initial fEPSP. The fEPSP decayed over a wide range of time courses, with median around 800ms. We estimate that the recovery time from ChR2 desensitization was around 150ms, again highly different between slices (Figure 7-figure supplement 2). Thus the mismatch detection was largely robust to ChR2 desensitization effects over the pulse train.”

      Thus, we do not expect any qualitative effect of ChR2 desensitization on our results for the following reasons. First, the mismatch experiments themselves take place under the conditions of this fibre activation decay, yet yield numerous cases of mismatch selectivity (Figure 7Eiii, Fi). Second, the simulations for mismatch (Figure 7,9) explicitly included ChR2 desensitization, and they too exhibit mismatch selectivity. Third, in this revision we have added a simulation for mismatch selectivity in the absence of ChR2 desensitization (Figure 9 panel E). There is no statistically significant difference from the reference model with ChR2 desensitization.

      Third, they do not know the responses of individual CA3 cells to stimulation. They do not know if each cell fires reliably during repetitive activation and whether each cell only fires once.

      We acknowledge that we collected limited data on this in the current dataset, because this analysis has already been published. In our previous work using a similar preparation we have characterized CA3 pyramidal neuron responses to grid optical stimuli in the CA3 (Figure 1b, e, g, Bhatia, Moza and Bhalla 2019) in response to the light pulse. These recordings show that cells fire with probabilities over a wide range from zero to one, but these firing probabilities are reliable over multiple trials. In this revision we have clarified this point:

      “Notably, the distribution of field amplitudes was tight (Figure 1E, Figure 1-figure supplement 2F), more so than the distribution of the corresponding EPSPs (Figure 1H). Together with previous work using a similar optical stimulus system and intracellular CA3 recordings (Bhatia et al. 2019), we interpret this to say that the spiking responses from CA3 neurons to optical stimuli were consistent from trial to trial.”

      Regarding number of action potentials: In the previous revision we had examined field data from our recordings to characterize the occurrence of multiple spikes (Figure 1-figure supplement 2). Second field peaks were common, at about 40% of the amplitude of the first, and within ~3.5 ms of the first peak. Third peaks were much smaller. From the text adjacent to Figure 1:

      We observed a small amount of ‘ringing’ of the field response which we interpret as either CA3 spiking in a burst, or recurrent activation of the CA3 neurons (Figure 1-figure supplement 2). The second peak was ~40% of the first, within 4 ms of the first. The ringing was down to ~5% by the third peak which occurred within ~9 ms of the flash. This sets an upper bound to any contribution to patterns by recurrence or multiple spikes during a ChR2 flash.

      On the basis of these field responses and previous reports (Bhatia et al. 2019), we infer that the CA3 neuron response to optical input was a brief event, with reliable probability of a spike on any given flash, and typically having one or occasionally two spikes within 6 ms of the light flash.

      Reviewer #3 (Public review):

      Summary:

      This work shows experimentally and computationally that single CA1 neurons can perform mismatch detection on patterned CA3 inputs and that STP and EI balance underlie this detection.

      Strengths:

      It has been known that STP can enhance the EPSP when the corresponding presynaptic input exhibits abrupt changes in firing rate. This work provides experimental evidence and further computational support for the hypothesis that the basic computation through STP is useful for detecting abrupt changes in the spatial pattern of synaptic inputs at the Schaffer collaterals. Further, their results indicate the novel view that mismatch detection is most efficient when gamma-frequency bursting inputs exhibit mismatches between theta cycles. The authors included novel results in the revised manuscript to show that the effective frequency range of gamma oscillation is broad, including both slow and fast gamma bands.

      In the initial submission, the dependence of mismatch detection performance on model parameters and experimental settings, such as pattern overlaps and other network parameters, was not sufficiently explored. In the revised manuscript, the authors extensively studied these points and summarized the novel results in Fig. 9. Furthermore, the authors clarified that jitters in input spikes can improve detection performance in some cases. These results show the robustness of their results against variations in external and internal conditions.

      Weaknesses:

      While this study shows an intriguing example of combined experimental and computational studies, some analytic results, for instance, regarding the complex contributions of jitters to detection performance, could have clarified the underlying mechanism deeper and further strengthened the manuscript.

      We acknowledge that the outcome of the jitter calculations looked complex. We redid this with substantially more repeats (30 simulated networks projecting to a CA1 neuron, rather than the original 6) and performed a cell-wise bootstrap analysis for error estimates for mean selectivity for each condition. This larger sample resulted in a smoother distribution of mismatch selectivity over different frequencies (Figure 7). We have carried out a further set of simulations over a range of jitter widths (Figure 7-figure supplement 3). We have added these findings to the paper:

      “We next asked how sensitive the mismatch detection was to spike jitter, since in-vivo neural activity has a distribution of spike timings. We reran our model with 30 distinct simulated networks projecting to a CA1 neuron, each for a range of jitter values from 0 to 10ms. We performed a cell-wise bootstrap analysis to obtain error estimates for mean selectivity for each condition. We find that for 5-square stimuli selectivity is high at 0 jitter for all frequencies, but surprisingly for 15-square, selectivity is higher when jitter is nonzero (Figure 7-figure supplement 3). We suggest that when synaptic inputs are synchronous their effects add sublinearly, so some degree of jitter causes larger EPSPs when there are many inputs, as in the 15-square case. Consistent with this, the centroid for selectivity was always at a higher jitter level for 15-square than for 5-square (Figure 7-figure supplement 3, bootstrap test, p < 0.001).

      For the 50-Hz stimulus, a 10ms jitter is half the stimulus interval, so the onset of patterns are quite smeared. Thus, as expected, selectivity fell both in 5 (bootstrap p<0.001) and 15-square (bootstrap p=0.005) cases for 50 Hz when jitter was larger than 4 ms. (Figure 7-Figure supplement 3).”

      While we appreciate that a further analytic examination of jitter would be interesting, we feel that it would have to be done on a much reduced system compared to ours, and is therefore outside the scope of the current paper.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      The authors primarily responded with words rather than experiments. Their response has been adequate for some issues, but I remain concerned about their methods and how they could impact their own results, and how they might lead others astray.

      (1) The paper by Klyachko and Stevens (2006) does a good job of addressing the temperature issue.

      (2) The issue of free calcium is important. They provide a calculation that says that 2 mM added calcium corresponds to 1.27 mM free calcium. This seems very low based on the low affinity buffers in their solution. I used a standard and widely used tool (MINTEQ) to calculate the free calcium in their external solution and it says that the levels are 1.6 mM free calcium. It should be noted that most reports of calcium in CSF are for total calcium rather than free, and the total values are in the 1.1-1.4 range. If buffering in CSF is comparable to ACSF than this corresponds to 0.88 to 1.12 free calcium. So either I am wrong (and MINTEQ), or the authors are wrong. If they are incorrect then it makes a big difference for their experiments. It would also be a very bad thing for slice electrophysiology to get this wrong. Also see the paper by Borst at the calyx of Held that says that 2 mM added external calcium typically used in ACSF (as used in the present study) is too high (PMID 2394036). For these reasons I am highly skeptical that their calcium levels match those in vivo.

      We agree that our calculations and understanding of calcium levels were incorrect. We used only two of the constituents of our buffer solution for our calculation of free calcium, which gave our previous low estimate of 1.27mM, whereas MINTEQ is much more complete. We redid our calculations using MINTEQ and obtained the same values as the reviewer, and we thank the reviewer for pointing out this resource. We also looked at a broader range of studies including those suggested by the reviewer and agree that the literature has flagged 2mM total calcium as too high. We have added a new section to the Introduction to reflect this body of work. We have removed our earlier free calcium calculations.

      Please see our detailed response to this point above, including reference to the literature and re-running of many simulations at physiological levels of calcium, to examine the effect on our conclusions. In brief, the mismatch selectivity is weakened at physiological calcium, but is still present.

      (3) The issue of showing a 2% per pulse decrease that they attribute to ChR2 desensitization is important. This would be sufficient to have a major influence on their experiments in which they use repetitive activation. What is the time course of recovery of this inability to stimulate all of the fibers?

      Please see our response to the same point above. In brief, we have looked at the experimental data for field epsp decline over extended stimuli, and have shown that it settles at about 70% of the starting level in the case with the steepest decline (Figure 7-figure supplement 2). The time-course of decline is about 0.8 seconds. From the frequency dependence of the decline, we estimate that the recovery time is around 150 ms (Figure 7 -figure supplement 2). We also point out that our experimental data as well as our simulations of mismatch selectivity were carried out in the presence of ChR2 desensitization, yet we observed clear mismatch responses.

      Can they get around this problem by lowering the stimulus intensity?

      From Mattis et al Figure 2a, ChR2 performance over pulse trains deteriorates with decreasing power. Hence we do not expect that lowering the stimulus intensity would help.

      From the methods:

      “We chose short light pulses for two reasons. First, to decrease jitter in the light-triggered CA3 neuron spike timings. Second, if one is below saturating light intensities, short pulses at higher power give more consistent spiking over multi-pulse trains (Mattis et al. 2012), Figure 2A, using a 2ms pulse width). Note that the Mattis study was done at 20-23℃ as compared to our ~33℃, and the authors state that ChR2 kinetics are strongly temperature dependent.”

      Are their results altered if they lower the illumination intensity by a factor of 2 or increase it by a factor of 2?

      We did not try this, as we were aiming for short light pulses to minimize spike timing jitter, and we needed high intensity stimulation to get strong EPSPs on the postsynaptic cells. Based on the Mattis study we expect that yes, the results would be altered, and we would get more reliable spiking at still higher intensities. However, we used 100% intensity (estimated 100mW/mm<sup>2</sup>) in our optical system, so we could not push it further.

      We did perform one manipulation to mitigate desensitization: For the 1-square illumination we sequenced illumination from one spot to another, before returning to the first spot, so as to minimize desensitization. From the methods:

      “In 1-square stimulation experiments, the CA3 cell layer was illuminated one spot at a time with an interstimulus time of 3 seconds to avoid desensitisation of channelrhodopsin.”

      (4) They base their reliability of stimulation on field recordings. They need to do extracellular measurements from CA3 cells to see how reliably each cell fires and if they only fire once or more that once during stimulation.

      We used prior published work reporting intracellular recordings from CA3 cells, and additional analysis of field data to address this point. In brief, the probability of a cell firing is reliable, but on individual trials it might not fire. Additionally, we estimate that following a light pulse, cells fire twice about 40% of the time but rarely 3 or more times. We provide a more detailed response to this concern above.

      If each cell does not fire reliably once and only once for each stimulus pulse it will alter use-dependent plasticity and call into question the whole result.

      We agree that, at the single synapse level, an accurate account of use-dependent plasticity depends on single reliable spikes. Our recordings from CA1 are a sum over many synapses, and hence we use population CA3 activity measures to characterize our inputs, with the stipulation that these are reliable over multiple trials. Bhatia et al, 2019 showed reliability at the action-potential level, and in the current study we show it at the field potential level (Figure 1-figure supplement 2 panel F). We use our models to bridge the gap between the single-synapse responses to individual trials, to whole-cell readouts (Figures 5, 6, 7), and apply several tests to show that this produces models that fit the data (Table 1).

      Our results, including EI balance and mismatch selectivity, are direct measurements in the presence of distributions of input APs. We point out in the discussion that:

      “In-vivo input activity to this circuit is complex, and on successive stimuli there will be a distribution of exact CA3 pyramidal cell AP timings, number of APs per cell, and number of activated axons. To the extent that model robustness (Figure 9, Figure 7-figure supplement 3) gives insights into the real system we suggest that our small model of network + single neuron model + presynaptic signaling chemistry captures some of the attributes of the biological system, which lead to such robustness. This robustness is almost a necessity in a system where the probability of release for individual synapses is in the range of 20% (Murthy et al 1997): any signal transmission or plasticity-type computation has to accommodate huge stochastic uncertainty on any given trial.”

      I remain highly skeptical of their methods and their experimental results.

      We hope that we have addressed the specific points of concern that have led to this scepticism.

    1. eLife Assessment

      This valuable study employs an innovative chemoproteomic and multi-modal experimental approach to support VDAC2 as a key functional mediator of STX effects on mitochondrial bioenergetics. The authors assimilated convincing evidence for targeting VDAC2 within POMC neurons to demonstrate neuroprotective therapies against Alzheimer's disease and menopause-associated neurodegeneration, with the potential to minimize adverse off-target side effects.

    2. Reviewer #2 (Public review):

      This study investigates STX, a non-steroidal, CNS-selective estrogenic compound known for its neuroprotective effects in stroke and Alzheimer's disease models. The authors successfully identify VDAC proteins, specifically VDAC1, VDAC2, and VDAC3, as the direct mitochondrial targets of STX. To characterize this interaction, the researchers employ a comprehensive approach combining chemoproteomics, single-cell qPCR, planar lipid membrane electrophysiology, and Seahorse metabolic flux analyses. The manuscript frames the physiological characterization of the STX-VDAC mechanism within POMC neurons, which serve as a metabolically sensitive and estrogen-responsive system, while appropriately positioning its specific role in neurodegeneration as a direction for future research.

      A major strength of this study is its innovative methodology, particularly the development of a novel bifunctional STX probe (BF-STX) featuring a photo-crosslinkable diazirine group and an alkyne handle to capture transient STX-protein interactions in living cells. The experimental design is rigorous, incorporating essential controls such as no-UV negative controls and competition assays with excess unlabeled STX. These controls provide convincing evidence that the identified VDAC interactions are genuine rather than nonspecific artifacts. Furthermore, the authors establish a solid physiological rationale for utilizing POMC neurons to investigate these estrogen-sensitive mitochondrial mechanisms. The mechanistic claims are well supported across multiple complementary platforms, offering a clear view of how STX affects VDAC channel properties and mitochondrial bioenergetics. The authors also demonstrate scientific rigor by properly scoping their conclusions and recognizing all three VDAC isoforms as candidate targets without overstating the exclusivity of any single isoform.

      The manuscript thoughtfully addresses the current boundaries of the study within the Discussion section. The authors transparently acknowledge their reliance on immortalized cell lines, correctly identifying the need for primary neurons or whole-animal models in future studies to fully establish the physiological significance of these findings. The study also notes that defining the exact structural binding site of STX and performing targeted loss-of-function experiments, such as neuron-specific VDAC manipulation, remain necessary next steps. Finally, the authors address the non-linear dose-response observed at higher STX concentrations by offering a logical, multi-target hypothesis to explain the complex data.

    3. Author response:

      The following is the authors’ response to the original reviews.

      We thank the editors and reviewers for their careful evaluation of our manuscript and for their constructive comments. We appreciate their recognition of the innovative chemoproteomic strategy and the complementary molecular, electrophysiological, and metabolic approaches used to identify voltage-dependent anion channels (VDACs) as mitochondrial targets of STX.

      In response to the reviews, we have revised the manuscript to strengthen the physiological rationale for studying POMC neurons; clarify the potential relevance of STX–VDAC signaling to neuronal mitochondrial function and neurodegeneration; distinguish our experimentally demonstrated mechanism from its potential disease relevance; clarify the contributions of individual VDAC isoforms; and more explicitly acknowledge the limitations of the present study.

      Our revised manuscript emphasizes that the principal finding of this study is the identification and functional characterization of an STX–VDAC mitochondrial mechanism in an estrogen-responsive neuronal system. Its relevance to neurodegenerative disease is presented as an important context and direction for future investigation rather than as a demonstrated disease mechanism.

      Public Reviews:

      Reviewer #1 (Public review):

      Major Concern

      (1) Lack of Rationale

      The study provides no justification for investigating sex-specific aspects of Alzheimer's disease by focusing on VDAC-mediated mitochondrial dysfunction in POMC neurons. These hypothalamic neurons are not recognized as early or primary sites of AD vulnerability, making the biological premise unclear.

      We thank the reviewer for raising this important point. We agree that the original manuscript did not sufficiently explain the rationale for using POMC neurons.

      Our rationale is primarily physiological rather than disease-specific. POMC neurons are a well-characterized, metabolically sensitive, and estrogen-responsive neuronal population in which membrane-initiated estrogen signaling and the actions of STX have been extensively characterized. They therefore provide a physiologically relevant neuronal system for identifying the molecular mechanisms through which STX regulates mitochondrial function.

      The potential relevance to neurodegeneration is supported by evidence that hypothalamic and POMC neuronal function can be disrupted in neurodegenerative disease models. Do and colleagues (2018) reported hypothalamic neurodegeneration, increased inflammatory and apoptotic markers, and reduced POMC neuronal populations in 3xTg-AD mice and further showed that exercise attenuated hypothalamic apoptosis and restored POMC neuronal populations (Do, Laing et al. 2018). In addition, Shen and colleagues (2016) demonstrated disruption of POMC/MC4R signaling in APP/PS1 mice and showed that restoration of this pathway improved synaptic function (Shen, Tian et al. 2016).

      These studies do not establish POMC neurons as a primary site of AD pathology, but they demonstrate that POMC-related neuronal systems can be vulnerable to neurodegenerative processes. This provides a broader biological context for investigating mitochondrial mechanisms in this neuronal population.

      There is also a strong physiological rationale for examining estrogen-sensitive mechanisms in POMC neurons. These neurons are established targets of 17β-estradiol and are highly responsive to metabolic and mitochondrial state. STX is a non-steroidal estrogenic compound that activates membrane-initiated estrogen signaling, and our previous studies demonstrated neuroprotective and mitochondrial effects of STX in a neurodegenerative disease model.

      Thus, POMC neurons were used because they provide a well-defined estrogen-responsive and metabolically sensitive neuronal population in which STX signaling can be mechanistically investigated—not because we consider them an initiating site of AD pathology.

      We have revised the Introduction and Discussion accordingly. The revised manuscript now emphasizes the physiological significance of STX–VDAC signaling for neuronal mitochondrial function and presents its potential relevance to neurodegeneration as an important area for future investigation.

      (2) Weak Link to AD Pathogenesis

      Although mitochondrial dysfunction is well established in AD, the authors do not convincingly demonstrate a mechanistic or pathological connection between VDAC2 and AD. VDACs are not established contributors to AD etiology, and the manuscript does not strengthen this association.

      We agree that the present experiments do not establish VDAC2 as an etiological or pathological driver of AD. This was not the objective of the present study.

      The primary goal was to identify the molecular target(s) through which STX influences mitochondrial function. Using BF-STX chemoproteomic capture and competition experiments together with single-cell gene-expression analysis, planar lipid membrane electrophysiology, and mitochondrial metabolic analyses, we identify VDAC proteins as mitochondrial targets of STX and demonstrate functional effects of STX on VDAC channel properties and mitochondrial bioenergetics.

      Our previous studies demonstrated neuroprotective and mitochondrial effects of STX in the 5xFAD model (Lee, Bostick et al. 2025), providing a neurodegenerative context that motivated the present mechanistic investigation. However, the current findings should not be interpreted as establishing VDAC2 as an AD pathogenic mechanism.

      We have revised the manuscript accordingly. The STX–VDAC interaction is now presented principally as a mitochondrial mechanism with potential relevance to neuronal physiology and neurodegeneration. Whether this pathway contributes to the neuroprotective actions of STX in disease models will require direct experimental testing.

      (3) Unclear Relevance to AD Contexts

      While the data support an interaction between STX and VDAC2 affecting mitochondrial parameters (ATP production, membrane potential, glycolysis, respiration) in POMC neurons, the study does not show whether this mechanism is relevant to mitochondrial dysfunction in AD. No validation is provided in AD-related models or in contexts related to sex-specific AD phenotypes.

      We agree that the present experiments do not directly establish the relevance of the STX–VDAC interaction to mitochondrial dysfunction in AD.

      The current study was designed to identify and characterize the molecular mechanism underlying the mitochondrial actions of STX, rather than to test this pathway in a specific neurodegenerative disease model. Our findings demonstrate that STX interacts with VDAC proteins, modifies VDAC channel properties, and alters mitochondrial bioenergetics.

      The study builds on our previous findings in the 5xFAD model, in which STX reduced amyloid-β-associated pathology and affected mitochondrial function (Lee, Bostick et al. 2025). The identification of VDAC proteins as STX targets therefore provides a mechanistic foundation for future studies examining whether this pathway contributes to neuronal protection under neurodegenerative conditions.

      We have revised the Discussion to acknowledge the absence of direct disease-model validation. Future studies using conditional or neuron-specific manipulation of VDAC isoforms will be important for determining the physiological and neuroprotective significance of STX–VDAC signaling in vivo.

      Accordingly, our conclusions now emphasize what is directly supported by the present experiments: STX interacts with VDAC proteins and modulates VDAC channel function and mitochondrial bioenergetics. The relevance of this mechanism to neurodegenerative disease remains to be established.

      (4) Interpretation of Competitive Binding Data

      The competitive binding results in Figure S4B are not adequately interpreted. The dose-dependent competition observed for VDAC3 suggests it may be a stronger candidate than VDAC2, yet this possibility is not addressed.

      We thank the reviewer for highlighting this important point. We agree that the original manuscript placed too much emphasis on VDAC2 based on the chemoproteomic data.

      Our chemoproteomic experiments identified VDAC1, VDAC2, and VDAC3 as STX-interacting proteins. Competition with unlabeled STX produced a particularly clear reduction in VDAC3 labeling, including complete loss of the VDAC3 signal at three molar equivalents of unlabeled STX. We therefore agree that VDAC3 represents an important candidate STX target.

      We have revised the Results and Discussion so that the competition experiment is no longer interpreted as demonstrating preferential or exclusive binding to VDAC2.

      However, the persistence of VDAC1 and VDAC2 labeling may also be influenced by properties of the BF-STX photoprobe as alkyl diazirine probes can preferentially photolabel membrane proteins (Kleiner, Heydenreuter et al. 2017). We now present this only as a possible technical consideration rather than an explanation established by our data.

      Our subsequent emphasis on VDAC2 was based on the integration of several observations. Single-cell qPCR demonstrated that Vdac2 is the predominant transcript in the native POMC neurons examined (revised Figure 3B), with an approximate expression hierarchy of Vdac2 > Vdac3 >> Vdac1. In addition on a technical note, recombinant VDAC3 is more difficult to reconstitute reliably into artificial membranes because of its lower stability in detergent.

      The revised manuscript therefore recognizes all three VDAC isoforms as candidate STX targets but does not claim that VDAC2 is the exclusive or preferential target. Direct comparisons of STX binding and functional modulation among the three isoforms will be required to establish isoform selectivity.

      Reviewer #2 (Public review):

      (1) Physiological Relevance of Cell-Line Experiments

      Most experiments were performed in immortalized cell lines rather than primary neurons or in vivo models, limiting their physiological relevance.

      We agree that the use of immortalized neuronal cell models represents an important limitation.

      However, these models provided the cellular material, reproducibility, and experimental control required for chemoproteomic target capture and Seahorse metabolic measurements. Importantly, we complemented these studies with single-cell analysis of native POMC neurons and electrophysiological characterization of recombinant VDAC channels.

      Nevertheless, these approaches do not substitute for direct demonstration of STX–VDAC signaling in primary POMC neurons or in vivo. We have therefore revised the Discussion to emphasize that establishing the physiological significance of this mitochondrial pathway will require validation in primary neuronal preparations and whole-animal models.

      (2) Structural Binding Site of STX on VDAC

      The exact structural binding site of STX on VDAC remains unresolved.

      We agree. BF-STX chemoproteomics identifies proteins interacting with STX in a cellular environment but does not resolve the amino acid residues or structural pocket responsible for binding.

      We have clarified this limitation in the Discussion. Determining the STX-binding site will require complementary approaches such as targeted mutagenesis, direct binding measurements with purified VDAC proteins, and structural studies in membrane-like environments, including lipid nanodiscs. Such studies should also determine whether STX recognizes a conserved feature among VDAC isoforms or exhibits isoform selectivity.

      (3) Lack of VDAC2 Loss-of-Function Experiments

      No loss-of-function experiments (e.g., VDAC2 knockdown) were performed to establish a direct causal link between VDAC2 and STX's bioenergetic and neuroprotective effects.

      We agree that VDAC loss-of-function experiments would provide an important additional test of causality.

      Our present evidence is convergent: chemoproteomics identifies VDAC proteins as STX-interacting targets; single-cell analyses demonstrate VDAC expression in POMC neurons; electrophysiological studies show that STX modifies VDAC channel properties; and metabolic analyses demonstrate STX-dependent changes in mitochondrial bioenergetics. Together, these findings support a STX–VDAC mechanism but do not establish that VDAC2 alone is necessary for the mitochondrial or neuroprotective actions of STX.

      We have revised the manuscript accordingly and explicitly identify the absence of loss-of-function experiments as a limitation.

      Because whole-body VDAC2 loss-of-function is associated with severe developmental consequences, future studies will require conditional or neuron-specific approaches. Parallel evaluation of VDAC1 and VDAC3 will also be important because all three isoforms were identified by chemoproteomics and potential functional redundancy may complicate single-isoform manipulations.

      (4) Non-linear Dose-Response at Higher STX Concentrations

      The non-linear dose-response at higher STX concentrations also requires further investigation.

      We agree that the non-linear concentration-response warrants further investigation and have revised the Discussion to avoid interpreting the STX response as a simple monotonic concentration-response relationship.

      One possibility is that STX engages multiple mitochondrial targets with different apparent affinities and opposing effects on respiration. At lower concentrations, STX may preferentially engage a higher-affinity target, potentially VDAC, whereas higher concentrations may recruit lower-affinity targets that constrain this response. Consistent with this possibility, our BF-STX dataset (Supplemental Tables) identified several mitochondrial proteins involved in oxidative phosphorylation and metabolite transport, including ATP5F1C, NNT, SLC25A4, SLC25A5 and SLC25A3. ATP5F1C is required for efficient mitochondrial ATP production (Fiorillo, Scatena et al. 2021), and estrogenic regulation of ATP synthase has been reported (Massart, Paolini et al. 2002, Moreno, Moreira et al. 2013). NNT, SLC25A4, SLC25A5 and SLC25A3 also regulate mitochondrial respiration, redox balance, and ATP production (Mayr, Merkel et al. 2007, Lopert and Patel 2014). However, BF-STX enrichment does not establish direct STX binding, relative affinity, or functional modulation of these proteins. We therefore present the multi-target explanation only as a hypothesis. Direct binding and concentration-dependent target-engagement studies will be required to determine whether the non-linear response reflects recruitment of a lower-affinity mitochondrial target, concentration-dependent effects on VDAC itself, or downstream mitochondrial feedback.

      We thank the editors and reviewers again for their constructive comments. The revised manuscript more clearly defines the physiological rationale for the experimental system, establishes the scope of the STX–VDAC mitochondrial mechanism supported by our data, and places its potential relevance to neurodegeneration in an appropriately forward-looking context.

      We hope that the revised manuscript and our responses satisfactorily address the concerns raised by the editors and reviewers.

      References

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      Fiorillo, M., C. Scatena, A. G. Naccarato, F. Sotgia and M. P. Lisanti (2021). "Bedaquiline, an FDA-approved drug, inhibits mitochondrial ATP production and metastasis in vivo, by targeting the gamma subunit (ATP5F1C) of the ATP synthase." Cell Death Differ 28(9): 2797-2817.

      Kleiner, P., W. Heydenreuter, M. Stahl, V. S. Korotkov and S. A. Sieber (2017). "A Whole Proteome Inventory of Background Photocrosslinker Binding." Angew Chem Int Ed Engl 56(5): 1396-1401.

      Lee, H.-J., Z. Bostick, J. Doherty, T. L. Swanson, M. J. Kelly, J. F. Quinn, N. E. Gray and P. F. Copenhaver (2025). "Neuroprotection against beta-amyloid toxicity by the novel estrogen receptor modulator STX requires convergent signaling pathways." Frontiers in Molecular Neuroscience Volume 18 - 2025.

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      Massart, F., S. Paolini, E. Piscitelli, M. L. Brandi and G. Solaini (2002). "Dose-dependent inhibition of mitochondrial ATP synthase by 17 beta-estradiol." Gynecol Endocrinol 16(5): 373-377.

      Mayr, J. A., O. Merkel, S. D. Kohlwein, B. R. Gebhardt, H. Böhles, U. Fötschl, J. Koch, M. Jaksch, H. Lochmüller, R. Horváth, P. Freisinger and W. Sperl (2007). "Mitochondrial phosphate-carrier deficiency: a novel disorder of oxidative phosphorylation." Am J Hum Genet 80(3): 478-484.

      Moreno, A. J., P. I. Moreira, J. B. Custódio and M. S. Santos (2013). "Mechanism of inhibition of mitochondrial ATP synthase by 17β-estradiol." J Bioenerg Biomembr 45(3): 261-270.

      Shen, Y., M. Tian, Y. Zheng, F. Gong, A. K. Y. Fu and N. Y. Ip (2016). "Stimulation of the Hippocampal POMC/MC4R Circuit Alleviates Synaptic Plasticity Impairment in an Alzheimer's Disease Model." Cell Rep 17(7): 1819-1831.

    1. eLife Assessment

      This important study investigates whether high-level physical reasoning is grounded in real-time bodily and vestibular signals using an innovative combination of virtual tool-use tasks and galvanic vestibular stimulation. The evidence is solid for the specific conclusion that ongoing vestibular information can influence selected aspects of physical reasoning in a context-dependent manner, while is incomplete for the broader mechanistic interpretations.

    2. Reviewer #1 (Public review):

      Summary:

      This study investigates a fundamental question in cognitive science: is our ability to reason about the physical world an abstract mental process, or is it "embodied"-directly rooted in our real-time physical interactions with the environment? The authors compared participants' performance in computerized reasoning games with and without Galvanic Vestibular Stimulation (GVS). They suggest that participants failed more often and utilized suboptimal strategies under GVS compared to a sham stimulation condition. Furthermore, they suggest that this detrimental effect of GVS was reduced when the games were governed by altered gravity (hyper- and hypo-gravity). Consequently, the authors conclude that the physical experience of the body modifies high-level cognitive skills, such as reasoning.

      Strengths:

      The manuscript is well-written, organized, and easy to follow. Also, combining a specialized physical reasoning task with real-time vestibular disruption (GVS) is an intriguing approach to testing the boundaries of embodied cognition.

      Weaknesses:

      The empirical evidence supporting the conclusions, as currently stated in the Abstract, remains weak. The overall effect of GVS is limited, and the conclusions regarding its impact on strategy use rely on sophisticated clustering and classification methods whose reliability and validity are not clearly demonstrated in the current manuscript. The differences between Studies 1 and 2, which are critical to establishing the effect of altered gravity, are also not robust overall.

    3. Reviewer #2 (Public review):

      Summary

      The paper investigates whether the real-time physical experience of the body shapes high-level physical reasoning. Participants played a set of computerized tool-use reasoning games (the Virtual Tools paradigm) in which they must use knowledge of physical laws - including gravity, collisions, and inertia - to guide a ball into a target area. In Study 1, participants played the games under terrestrial gravity while receiving either Galvanic Vestibular Stimulation (GVS), which introduces noise into the vestibular organ and disrupts gravitational signalling, or a Sham condition with matched skin sensation. In Study 2, a separate cohort played the same games redesigned under hypogravity (0.5 g - half Earth g) or hypergravity (2 g - double Earth g), again with concurrent GVS or Sham stimulation. Performance was assessed through success rate, number of attempts, and time per attempt; strategy was assessed through the spatial distance between successive tool placements and the frequency of tool switching across attempts. A post-hoc gravity-weighted index (GWI) was computed to compare the effect of vestibular perturbation across the two studies. The main finding is that GVS impairs performance in gravity-dependent games under terrestrial gravity, yet the same perturbation appears to be neutral or even beneficial when the game environment involves non-terrestrial gravity - a result the authors interpret as evidence for an adaptable, body-grounded internal model of physics.

      Strengths

      One of the most notable strengths of this work is its conceptual positioning at the intersection of embodied cognition and physical reasoning. Rather than treating the human body either as an abstract information-processing device or as a purely biomechanical system, the authors take seriously the idea that cognition is scaffolded by ongoing sensorimotor state - and they test this idea with a paradigm that is both tractable and theoretically motivated. The use of the Virtual Tools paradigm is well-suited to this goal: the games vary systematically in their reliance on gravitational predictions, allowing selective impairment (rather than general disruption) to serve as a signature of embodied physical reasoning.

      The dual-study design is another strength. Testing the same vestibular perturbation under terrestrial and altered game-gravity conditions and observing a reversal in its effect depending on context, provides a form of internal control that is conceptually compelling. The additional clustering analyses (Dirichlet Process Gaussian Mixture Model and leave-one-out kernel density classification) strengthen the strategy results beyond raw distance measures, confirming that GVS systematically shifts participants' spatial exploration strategies.

      The paper is also clearly written and engages meaningfully with relevant theoretical frameworks - predictive coding, embodied cognition, and stochastic resonance - making it accessible and stimulating for a broad audience.

      Weaknesses

      (1) Absence of multiple-comparisons correction. A large number of game-level pairwise t-tests are conducted in both studies (upward of twenty per study) without correction for familywise error rate. The game-level effects that anchor the main narrative - in Study 1 alone: Remove, GoalMove, Spiky, Falling_A, Shafts_B, Gap, and Chaining - arise from an uncorrected pool of comparisons. The probability that some of these constitute false positives is non-trivial. The authors should apply a correction (e.g., Benjamini-Hochberg) or at minimum discuss this limitation explicitly.

      (2) The facilitation claim rests on a post-hoc and arbitrarily parameterized index. The gravity-weighted index (GWI), which drives the central cross-study comparison uses integer coefficients (1, 2, 3) to weight games by gravity dependency level. These coefficients are entirely arbitrary and bear no principled relationship to the actual gravitational magnitudes used in the study. Why not use the gravity dependency ratings themselves, or the empirically estimated gravity impact scores from the computational modelling mentioned in the Methods? The choice of weights should be either principled or tested across a range of values to demonstrate robustness. Furthermore, the notation in equation (1) as currently typeset reads as "Gravity minus Weighted Index" rather than "Gravity-Weighted Index"; this should be corrected.

      (3) The "facilitation" interpretation exceeds what the data in Study 2 directly support. Across all games in Study 2, GVS versus Sham differences in absolute performance are non-significant in all directions. The facilitation claim derives entirely from the GWI being higher in Study 2 than in Study 1 - a between-subjects comparison involving different participant groups and a non-pre-registered metric. The language of "facilitation" should be tempered accordingly, or the authors should provide additional analyses to support this framing.

      (4) Gravitational manipulation is visual only, and the vestibular system is only one component of the gravity-sensing network. Gravity perception results, as the authors very well know, from a distributed multisensory integration process that involves, in addition to the vestibular system, visual, proprioceptive, and visceral inputs. The present paradigm manipulates gravitational context solely through visual cues and targets the vestibular system through GVS - a point the authors acknowledge but do not discuss in sufficient depth. It is important to distinguish clearly between real gravitational alterations (as achieved in parabolic flight or centrifuge environments, where the entire body is physically subjected to a different gravitational vector) and virtually altered gravity, where only one sensory modality is targeted while others remain anchored to 1 g. The scope of the conclusions should reflect this distinction.

      (5) The choice of 0.5 g and 2 g may lack sensitivity. Combining the two altered-gravity conditions in Study 2 because no significant effect of hypo versus hypergravity was found is statistically pragmatic but conceptually unsatisfying. There is evidence in the space physiology literature that gravitational processing is not linearly symmetric around 1 g: threshold effects exist below and above terrestrial gravity that may not be captured by modest deviations (half and double g) - see refs below. It is worth discussing whether the absence of a hypo/hyper distinction in Study 2 reflects a genuine equivalence or a lack of sensitivity, and whether more extreme conditions (e.g., near-zero g or 4-5 g) might reveal different processing regimes. Whether 0.5 g and 2 g were sufficient to saturate the system or merely insufficient to perturb it remains an open question with direct implications for the interpretation of the null GWI effects on strategy measures.

      Lee SMC, Ribeiro LC, Martin DS, Zwart SR, Feiveson AH, Laurie SS, Macias BR, Crucian BE, Krieger S, Weber D, Grune T, Platts SH, Smith SM, and Stenger MB. Arterial structure and function during and after long-duration spaceflight. J Appl Physiol (1985) 129: 108-123, 2020.

      de Winkel KN, Clément G, Groen EL, and Werkhoven PJ. The perception of verticality in lunar and Martian gravity conditions. Neurosci Lett 529: 7-11, 2012.

      Clément G, Moore ST, Raphan T, and Cohen B. Perception of tilt (somatogravic illusion) in response to sustained linear acceleration during spaceflight. Exp Brain Res 138: 410-418, 2001.

      Benson AJ, Kass JR, and Vogel H. European vestibular experiments on the Spacelab-1 mission: 4. Thresholds of perception of whole-body linear oscillation. Exp Brain Res 64: 264-271, 1986.

      (6) High-level reasoning is not defined with sufficient precision. The term "high-level reasoning" appears from the title onward and in the heading of the Study 1 results section (line 138), but it is never formally defined. The reader needs a clearer account of what distinguishes high-level physical reasoning from low-level sensorimotor prediction, and where the games used here fall along that continuum. What specific physical competencies - ballistic trajectories, free-fall predictions, collision dynamics, frictional forces, inertial effects - are required across the game set? When describing the subset of games that drive key effects, this information is critical for evaluating whether effects are specific to gravity reasoning or to some other physical concept.

      (7) Performance measures are disconnected from underlying kinematics. The performance measures (success rate, number of attempts, time per attempt) are coarse, high-level summaries. Time per attempt is used as a proxy for performance efficiency, yet participants received no instructions regarding speed, and different individuals may have adopted systematically different speed-accuracy trade-offs. It would be valuable to know whether time per attempt correlates with attempt number within a given game (which would indicate within-game learning) and whether mouse movement data - trajectory, velocity, hesitation - were recorded and could be analysed to provide more mechanistic insight into strategy formation.

      Comments on revised version.

      My overall assessment remains positive. I consider the findings important and the evidence solid. The study uses an original experimental approach to address a theoretically significant question, and I consider the data sufficient to support the more constrained conclusion that ongoing vestibular information can influence physical reasoning in a context-dependent manner.

      I agree with the other reviewers that the game-level multiple comparisons require appropriate statistical treatment and that the Gravity-Weighted Index should not carry more inferential weight than justified by its construction. I also agree that some of the broader mechanistic interpretations should be presented cautiously. These issues concern primarily the strength and scope of interpretation rather than the validity of the experimental findings themselves.

      I do not, however, consider the absence of preregistration of the exploratory analyses to constitute a methodological weakness in itself. Preregistration is desirable when analyses are intended to provide confirmatory evidence, but it is not a prerequisite for a scientifically informative exploratory analysis. The important distinction is that exploratory analyses should be transparently identified as such and their conclusions should be appropriately qualified. In my view, the relevant concern here is therefore the inferential weight placed on these analyses, rather than the fact that they were not preregistered.

      I would consequently retain a rating of solid for the strength of evidence rather than incomplete. The main claims are supported provided they are formulated at an appropriate level of specificity; the evidence is less secure for the broader mechanistic claims concerning general recalibration of internal models of gravity.

    4. Reviewer #3 (Public review):

      Summary

      This manuscript investigates whether high-level physical reasoning is influenced by ongoing vestibular and bodily signals rather than operating as a fully abstract process. The authors combine Galvanic Vestibular Stimulation (GVS) with the Virtual Tools task to test the effects of vestibular perturbation during reasoning under terrestrial gravity in Study 1 and visually simulated altered gravity in Study 2. The experimental approach is original and provides a potentially useful bridge between research on vestibular processing, physical cognition, and embodied reasoning.

      The revised manuscript is substantially improved. The authors have responded carefully to many of the methodological concerns raised in the previous review. In particular, they have removed the original arbitrarily weighted Gravity-Weighted Index, introduced mixed-effects models that account for participant-level and game-level variability, clarified the basis of the gravity-impact classification, and more clearly distinguished preregistered from exploratory analyses. The interpretation of the strategy measures and the treatment of the 0.5g and 2g conditions have also been improved. These revisions provide a substantially stronger and more transparent analytical framework for evaluating the hypotheses.

      Strengths

      The principal strength of the study is its conceptual and methodological originality. Combining vestibular stimulation with a relatively high-level physical problem-solving task provides an innovative approach to testing whether ongoing bodily information is associated with physical reasoning. The comparison of games that differ in their dependence on gravity is also potentially informative for distinguishing general effects of vestibular perturbation from effects more specifically associated with gravity-relevant reasoning.

      The revised analyses represent another important strength. The mixed-effects models provide a more principled basis for inference than reliance on individual game-level comparisons alone, while the revised Gravity Index removes the arbitrary weighting used in the original manuscript. The clearer treatment of exploratory analyses also makes it easier to distinguish the primary evidence from complementary observations. Overall, the revision has addressed many of the methodological concerns raised previously and has made the empirical pattern considerably easier to evaluate.

      Weaknesses

      The main remaining concern is the correspondence between the strength of the empirical evidence and the strength of the manuscript's central claims.

      In Study 1, the aggregate measures provide little evidence for an overall effect of GVS on performance or strategy. More importantly for the proposed gravity-specific account, the newly introduced mixed-effects analyses do not show significant stimulation-by-gravity-impact interactions for the principal performance or strategy measures. Some gravity-level-specific and game-level effects are present, including a difference in placement distance for high-gravity-impact games, and the exploratory spatial analyses provide additional evidence that vestibular perturbation may influence aspects of strategy. These findings are potentially informative, but the overall pattern is selective rather than a consistent and convergent demonstration that greater dependence on gravity systematically increases the detrimental effect of GVS.

      The evidence in Study 2 is more limited. Aggregate differences between GVS and Sham are small across the main performance and strategy measures, and the mixed-effects analyses do not identify reliable stimulation effects across the low-, medium-, or high-gravity-impact categories. Study 2 therefore does not directly demonstrate that GVS facilitates physical reasoning under altered gravity.

      The cross-study Gravity Index analyses provide some evidence that the effects may differ across gravitational contexts, but the pattern remains heterogeneous. For success rate, the high-gravity-impact contrast is in the predicted direction but remains a directional trend, with the credible interval including the point of equivalence. Other performance measures do not provide convergent support for the same pattern. The distance index shows reliable cross-study contrasts at some gravity-impact levels, but these form a crossover pattern rather than a simple reduction of the GVS effect as gravity dependence increases. The principal study-by-gravity-impact interactions are also not robust. In addition, this Gravity Index analysis was not preregistered and compares two separate participant samples. Taken together, these findings are more consistent with suggestive evidence for a context-dependent influence of vestibular perturbation than with a definitive demonstration of facilitation or rapid adaptation under altered gravity.

      A second concern is the mechanistic interpretation. The experiment directly measures behavioral performance and strategy under GVS and Sham conditions. It does not directly measure the strength of a terrestrial gravity prior, prediction errors, sensory reweighting, internal-model updating, or stochastic resonance. Predictive coding and related frameworks provide plausible interpretations of the findings, but the present behavioral data cannot distinguish among these mechanisms. Statements that GVS downregulates a terrestrial gravity prior, accelerates revision of an internal model, recalibrates cognitive expectations, or provides a vestibular "teaching signal" therefore imply a level of mechanistic specificity that is not directly established by the current experiments.

      Overall, the study remains innovative and potentially valuable, and the revision has substantially strengthened the analytical framework. The empirical results support a more circumscribed conclusion that vestibular perturbation can influence selected aspects of physical reasoning and strategy, with suggestive evidence that these effects may vary across gravitational contexts. The evidence is less conclusive for the broader claims that real-time embodied experience generally determines high-level physical reasoning or that vestibular perturbation facilitates rapid adaptation through specific mechanisms of gravity-prior updating.

    5. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      (R1.1) Lack of Overall Effects and Inflated Type I Error for Game-Level Effects For Study 1, to identify condition differences at the game level (i.e., Figure 2), the authors effectively conducted 70 independent t-tests (5 measures × 14 games). While 7 significant results were reported, this large number of independent tests invites an inflated Type I error rate, as no multiple-comparison correction appears to have been applied. A similar inflation is expected in Study 2, where 50 independent t-tests (5 measures × 10 games) yielded 5 significant comparisons (Figure 4). Although the authors might argue the direction of the differences is systematic, implying GVS generally impairs performance, at least one significant comparison shows the opposite effect: tool switching indicates that GVS led to better performance for the 'Table_A' game in Study 2 (Figure 4d), whereas the same variable indicated GVS led to worse performance in Study 1 (Figure 2d). I suspect that none of the significant game-level results would survive a proper statistical correction. If possible, the authors can redo statistical testing with corrections (FDR or Bonferroni) or with LMM using game as a random effect. Before proper statistical analyses, I strongly encourage the authors to refrain from drawing broad conclusions based on these isolated game-level results.

      We thank the reviewer for this important statistical point. We have revised the manuscript to clarify that each game was treated as a distinct physical-reasoning problem, and these were used to localize the games in which the stimulation effect was expressed, not to compare games with one another or to draw broad conclusions from isolated contrasts (and therefore, in this case, no multiple-comparison corrections are required; lines 637-640). That said, to address the reviewer’s concern directly and to strengthen our conclusions in the paper, we also added mixed-effect models using gravity impact level and stimulation type as fixed factors and participant and game as random effects for each study (lines 168-177, 202-207, 257-259, 301-303, 641-651, Tables 2 and 4).

      (R1.2) Furthermore, when analyzing data across all games, the study found no significant effect of GVS on overall performance or strategy measures in either Study 1 or Study 2. This lack of an aggregate effect contradicts the authors' conclusion that participants failed more often or utilized suboptimal strategies under GVS.

      We thank the reviewer for raising this issue. We agree that the analyses collapsed across all games did not reveal significant overall effects. However, this pattern is informative rather than contradictory: the full-game analysis combining gravity-dependent problems with games in which gravity does plays little or no role in the solution (8 low-gravity-impact games in Study 1 and 4 in Study 2; Extended Data Table 1). If GVS had produced a general dizziness-related, physiological, attentional, or arousal effect, we would expect a broad impairment across the task. Instead, the revised mixed-effects models show that the effects were tied to gravity-relevant contexts: in Study 1, GVS was associated with a trend-level increase in attempts for medium-gravity-impact games and a significant increase in placement distance for high-gravity-impact games. These effects were not observed in Study 2. Together with the cross-study Gravity Index analyses, this pattern strengthens the interpretation that GVS did not cause a general impairment, but selectively altered physical reasoning as a function of the gravitational context. We have now clarified this point in the manuscript (lines 398-403).

      (R1.3) Missing Rationale for Classification Analysis.

      It is puzzling why the authors pursued two exploratory analyses on tool placement after revealing that the two related primary measures (tool positioning and switching) did not generate significant condition differences in Study 1. These additional analyses-the Dirichlet Process Gaussian Mixture Model and leave-one-out classification-were not pre-registered. In the absence of overall condition differences, the authors appear to be "doubling down" by applying sophisticated classification tools to the raw data without a clear prior rationale.

      We thank the reviewer for this important comment. We agree that the rationale for these additional analyses needed to be made clearer. Our aim was not to “double down” on the primary measures, but to test a distinct question that the preregistered summary measures cannot address. The distance and tool-switching measures reduce each participant’s behaviour to two averages; they therefore do not capture the multivariate structure of spatial placements, whether participants move between distinct placement strategies across successive attempts, or whether the overall placement distributions differ systematically between stimulation conditions. Our analyses, therefore, included these two complementary analyses, adapted from prior Virtual Tools work (Allen et al., 2024), to characterise the spatial and sequential organisation of tool-use strategies. The Dirichlet Process Gaussian Mixture Model is well suited to this question because it identifies strategy clusters directly from x/y placement coordinates without imposing an a priori number of strategies, allowing us to quantify transitions between spatial strategies across attempts (in relation to the environment in the game which is not taken into account in the more basic measures). The leave-one-out kernel-density analysis then asks a related but independent question: whether the placement patterns contain sufficient condition-specific structure to classify stimulation condition above chance. These analyses therefore add value by testing the organisation of strategy use at a level not captured by the primary summary measures. We have revised the manuscript to clarify this rationale and to present these analyses as complementary evidence about strategy organisation, while keeping the mixed-effects models as the primary inferential basis for the manuscript (lines 216-218, 721-731, 752-753).

      (R1.4) Insufficient Evidence for the Reduced Effect of GVS Under Altered Gravity… To compare Study 1 and Study 2, the authors devised a "gravity-weighted index," but its definition is not sufficiently justified. The index assigns weights of 1, 2, and 3 to low-, medium-, and high-gravity-dependent games, respectively. The choice of these specific weights appears arbitrary, making the quantitative results difficult to interpret. More importantly, there is no citation or explanation regarding how these three levels of "gravity impact" were defined in the first place (Line 468). This index was also not pre-registered.

      We thank the reviewer for this important comment. We agree that the original gravity-weighted index was not sufficiently justified because it combined the three gravity-impact levels, using numerical weights of 1,2, and 3. Although these weights followed the ordinal classification of the games, they were not independently validated and could therefore make the cross-study comparison difficult to interpret. To address this concern directly, we removed the gravity-weighted index entirely. In the revised manuscript, we instead use a Gravity Index that is calculated separately for each measure and each gravity-impact level, as the ratio between performance or strategy outcomes under GVS and Sham. This removes the arbitrary weighting scheme and provides a direct estimate of the stimulation effect within each gravity-impact category.

      We then compared these Gravity Indexes between Study 1 and Study 2 using mixed-effect models that include study and gravity-impact level as fixed factors, rather than collapsing across gravity-impact levels with our defined weights. We used participant as random factor. Because the indexes are bounded and showed clustering at structural values, we analysed them using Bayesian cumulative logit models, which are appropriate for bounded, non-normally distributed outcomes and allow the cross-study contrasts to be estimated without imposing a linear weighting structure. We also clarified how the gravity-impact categories were defined, based on piloting and previous computational modelling work in the lab, and report the classification of each game in Extended Data Table 1. These revisions directly address the reviewer’s concern by replacing the arbitrary weighted composite with a transparent, category-specific analysis of the GVS effect across terrestrial and altered-gravity contexts, in line with frameworks outlined by James et al. (2021), Li et al. (2023), Liu et al. (2017) (lines 140-147, 276-292, 307-317, 588-594, 658-666, 692-717, Table 5, Fig 5, Extended data Fig 3).

      James, N. T., Harrell Jr, F. E., & Shepherd, B. E. (2021). Bayesian cumulative probability models for continuous and mixed outcomes. arXiv preprint arXiv:2102.00330.

      Li, C., Tian, Y., Zeng, D., & Shepherd, B. E. (2023). Asymptotic properties for cumulative probability models for continuous outcomes. Mathematics, 11(24), 4896.

      Liu, Q., Shepherd, B. E., Li, C., & Harrell Jr, F. E. (2017). Modeling continuous response variables using ordinal regression. Statistics in medicine, 36(27), 4316-4335.

      Manuguerra, M., Heller, G. Z., & Ma, J. (2020). Continuous ordinal regression for analysis of visual analogue scales: The R package ordinalCont. Journal of Statistical Software, 96, 1-25.

      (R1.5) The authors state that for the success rate index, a value close to -1 indicates a large negative difference for GVS, 0 indicates no difference, and 1 indicates a large positive difference. These are theoretical bounds; the actual distribution of each index should be examined to validate such claims. However, the paper lacks descriptive statistics for this composite index.

      We removed the composite index (See R1.6) and included the actual distribution of each index for the two studies in Extended Data Fig. 3.

      (R1.6) Notably, the "reduction" of the GVS effect in altered gravity was only demonstrated in one of the five available indices (success rate, p = 0.046). In fact, the success rate in Study 2 was 66.7(sham) vs 67.3 (GVS) in Table 2. It is highly debatable whether this marginal result justifies the conclusion that GVS effects "were reduced when the games included reasoning about altered gravity.

      We thank the reviewer for this important point. We agree that the original wording placed too much weight on a single marginal comparison and could be read as implying a direct overall benefit of GVS in Study 2. We have therefore revised both the analysis and the interpretation. The conclusion no longer rests on the absolute GVS–Sham difference in Study 2 alone, which was small when averaged across all games. Instead, we now ask a more targeted question: whether the relative effect of GVS differs between terrestrial and altered-gravity contexts as a function of the games’ gravity impact. To address this, we replaced the previous gravity-weighted index with separate Gravity Indexes for each measure and each gravity-impact level, and compared these indexes across the two studies.

      This revised analysis provides a more precise pattern. For success rate, the high-gravity-impact games showed evidence in the predicted direction, with a higher GVS/Sham index in Study 2 than in Study 1, suggesting that the negative effect of vestibular disruption observed under terrestrial gravity was reduced when participants reasoned about altered gravity (lines 285-287, Fig. 5, Table 5). For strategy, the distance index also showed gravity-impact-dependent differences between studies, indicating that GVS affected spatial strategy organisation differently depending on the gravitational context (lines 307-315; Fig. 5; Table 5). We have also tempered the relevant claims in the Discussion—rather than arguing for a broad facilitation effect, we now conclude that GVS has a context-dependent influence on physical reasoning, with detrimental effects under terrestrial gravity and reduced or relatively more favourable effects when reasoning must be adapted to altered gravity. Finally, we explicitly note that the time index should be interpreted cautiously because participants were not instructed to prioritise speed, making speed–accuracy trade-offs difficult to interpret (lines 335-336, 420-436).

      (R1.7) Questionable Assumptions Regarding Strategy. The authors assume that "big changes in tool positioning and frequent tool switching indicate poor evaluation of the failed outcome". This assumption is questionable. In solving this cognitive task, participants must explore and exploit solutions based on feedback. Large shifts in positioning or frequent tool switching might reflect active, adaptive exploration based on failed outcomes rather than a failure to evaluate them.

      We thank the reviewer for this comment. We agree that large changes in tool placement or tool switching are not inherently maladaptive, because exploration is an essential part of trial-and-error learning in the Virtual Tools task. Our interpretation is more specific: when these measures are averaged across attempts within a game, persistent large shifts in placement and frequent switching provide a proxy for reduced convergence on an effective strategy, rather than for any single exploratory move being “poor”. We have revised the manuscript to make this interpretation more nuanced (lines 194-197).

      We also clarified that this interpretation is grounded in our and others’ previous work using the same Virtual Tools task, in which these strategy measures systematically tracked improved reasoning across development and reduced reasoning efficiency in ageing (Grandchamp des Raux et al., 2024; Grandchamp des Raux et al., 2025; Dexter et al., 2025; Allen et al. 2023). Thus, although exploration can be adaptive, the pattern captured by these averaged measures has empirical validity as an index of how efficiently participants refine their strategy after feedback. We have therefore revised the wording to avoid implying that all switching or repositioning reflects poor evaluation, while retaining the use of these measures as established indicators of strategy organisation in this task (lines 194-197).

      (R1.8) Confounding Factors in GVS Interpretation… GVS does not selectively target the vestibular nerve; it also activates distributed fronto-parietal attention networks and hippocampal circuits essential for any reasoning task. Additionally, the vestibular system is linked to the limbic system and the cerebellum, which regulate emotional reactivity and arousal. Because attention and emotion are likely affected by GVS, the authors should be much more cautious in attributing their behavioral findings solely to changes in the "physical experience of the body.

      We thank the reviewer for raising this important point. We agree that GVS should not be interpreted as targeting the vestibular nerve in isolation, because vestibular signals are embedded within a distributed cortical and subcortical network that also contributes to attention, spatial cognition, arousal, emotion, and executive control. We have therefore revised the manuscript to acknowledge this broader vestibular-cognitive network explicitly, citing Ferrè and Haggard (2020), and to avoid attributing the behavioural effects solely to a narrow vestibular mechanism (lines 406-407).

      At the same time, the pattern of results is not consistent with a general attentional, arousal-related, or discomfort-driven impairment. If GVS had mainly disrupted participants’ general cognitive state, we would expect a broad reduction in performance across games and across gravitational contexts. Instead, we did not observe a general GVS effect across all games, and the effects were tied to the gravity relevance of the task and to the gravitational context: in Study 1, GVS affected measures most clearly in games with medium or high gravity impact, whereas the same pattern was not observed in Study 2 under altered-gravity reasoning. This selectivity strengthens the interpretation that the effects are related to gravity-dependent physical reasoning rather than to a non-specific physiological consequence of stimulation. We have revised the Discussion to make this point more explicit while still acknowledging that GVS acts on a broader vestibular-cognitive system rather than on a single isolated pathway (lines 398-410, 416-419).

      Ferrè, E. R., & Haggard, P. (2020). Vestibular cognition: State-of-the-art and future directions. Cognitive neuropsychology, 37(7-8), 413–420. https://doi.org/10.1080/02643294.2020.1736018

      Reviewer #2 (Public review):

      (R2.1) Absence of multiple-comparisons correction. A large number of game-level pairwise t-tests are conducted in both studies (upward of twenty per study) without correction for familywise error rate. The game-level effects that anchor the main narrative - in Study 1 alone: Remove, GoalMove, Spiky, Falling_A, Shafts_B, Gap, and Chaining - arise from an uncorrected pool of comparisons. The probability that some of these constitute false positives is non-trivial. The authors should apply a correction (e.g., Benjamini-Hochberg) or at a minimum discuss this limitation explicitly.

      We thank the reviewer for their comment. As we answered to R1.1, We have revised the manuscript to clarify that each game was treated as a distinct physical-reasoning problem, and these were used to localize the games in which the stimulation effect was expressed, not to compare games with one another or to draw broad conclusions from isolated contrasts (and therefore, in this case, no multiple-comparison corrections are required; lines 637-640). That said, to address the reviewer’s concern directly and to strengthen our conclusions in the paper, we also added mixed-effect models using gravity impact level and stimulation type as fixed factors and participant and game as random effects for each study (lines 168-177, 202-207, 257-259, 301-303, 641-651, Tables 2 and 4).

      (R2.2) The facilitation claim rests on a post-hoc and arbitrarily parameterized index…These coefficients are entirely arbitrary and bear no principled relationship to the actual gravitational magnitudes used in the study. Why not use the gravity dependency ratings themselves, or the empirically estimated gravity impact scores from the computational modelling mentioned in the Methods? The choice of weights should be either principled or tested across a range of values to demonstrate robustness.

      The reviewer raises an excellent point. As described in our response to R1.4, we agree that the original gravity-weighted index was not the best way to test the cross-study effect, because assigning numerical weights of 1, 2, and 3 to low-, medium-, and high-gravity-impact games imposed an interval structure that was not independently validated. We therefore removed this weighted index entirely.

      We also considered the reviewer’s suggestion of using the gravity-dependency ratings or computational gravity-impact estimates directly. However, these values were designed to classify and balance games according to the extent to which their solution depends on gravity; they were not calibrated as an interval-scale measure of the expected GVS effect, nor are they commensurable across the different outcome measures. Using them as continuous weights would therefore reintroduce the same problem: it would treat a stimulus-classification variable as a quantitative weighting parameter. Similarly, weighting by the physical gravity magnitudes used in Study 2 would not be appropriate, because gravity magnitude (0.5g vs 2g) and gravity dependency of a game are conceptually distinct: the former defines the simulated environment, whereas the latter defines how much solving a given game relies on gravity-based reasoning.

      To address the issue more directly, we now compute a Gravity Index separately for each measure and each gravity-impact level. We then compare these indices between Study 1 and Study 2 using models that include study and gravity-impact level as factors, rather than collapsing across games with author-defined weights. This approach preserves the theoretical distinction between low-, medium-, and high-gravity-impact problems while avoiding arbitrary parameterisation. We have revised the Methods, Results, and Discussion accordingly (lines 140-147, 276-292, 307-317, 588-594, 658-666, 692-717; Table 5; Fig. 5; Extended Data Fig. 3).

      (R2.3) Furthermore, the notation in equation (1) as currently typeset reads as "Gravity minus Weighted Index" rather than "Gravity-Weighted Index"; this should be corrected.

      We followed the reviewer’s comment and adjusted the notation in equation 1 (lines 702-706).

      (R2.4) The "facilitation" interpretation exceeds what the data in Study 2 directly support. Across all games in Study 2, GVS versus Sham differences in absolute performance are non-significant in all directions. The facilitation claim derives entirely from the GWI being higher in Study 2 than in Study 1 - a between-subjects comparison involving different participant groups and a non-pre-registered metric. The language of "facilitation" should be tempered accordingly, or the authors should provide additional analyses to support this framing.

      The reviewer is correct that more support is needed. We now strengthened the facilitation argument by adding a new mixed-effect models that includes gravity impact as a factor, by showing that: (1) while in study 1, GVS has a detrimental effect in some of the performance and strategy measures, in study 2 none of the GVS vs Sham differences is significant (lines 172-177, 205-207, 257-259, 301-303); and (2) the comparison of the GI between the 2 studies pinpoint to a facilitation role limited to games where gravity matters (284-287, 308-315). At the same time, we have tempered the language in the Discussion by avoiding a broad claim of facilitation and instead stating that the evidence points to a context-dependent effect of GVS, with detrimental effects under terrestrial gravity and reduced or relatively more favourable effects under altered-gravity reasoning, with moderate evidence for the success-rate index (lines 333-336). Thus, the revised manuscript directly addresses the reviewer’s concern in two ways: the interpretation no longer depends on a single aggregate Study 2 comparison, and the facilitation language has been replaced by a more precise conclusion supported by the new gravity-impact-level analyses.

      (R2.5) Gravitational manipulation is visual only, and the vestibular system is only one component of the gravity-sensing network. Gravity perception results, as the authors very well know, from a distributed multisensory integration process that involves, in addition to the vestibular system, visual, proprioceptive, and visceral inputs. The present paradigm manipulates gravitational context solely through visual cues and targets the vestibular system through GVS - a point the authors acknowledge but do not discuss in sufficient depth. It is important to distinguish clearly between real gravitational alterations (as achieved in parabolic flight or centrifuge environments, where the entire body is physically subjected to a different gravitational vector) and virtually altered gravity, where only one sensory modality is targeted while others remain anchored to 1 g. The scope of the conclusions should reflect this distinction.

      We thank the reviewing for raising this point and clarified the distinction in the introduction and discussion (lines 104-108, 353-356).

      (R2.6) The choice of 0.5 g and 2 g may lack sensitivity. Combining the two altered-gravity conditions in Study 2, because no significant effect of hypo versus hypergravity was found, is statistically pragmatic but conceptually unsatisfying. There is evidence in the space physiology literature that gravitational processing is not linearly symmetric around 1 g: threshold effects exist below and above terrestrial gravity that may not be captured by modest deviations (half and double g) - see refs below. It is worth discussing whether the absence of a hypo/hyper distinction in Study 2 reflects a genuine equivalence or a lack of sensitivity, and whether more extreme conditions (e.g., near-zero g or 4-5 g) might reveal different processing regimes. Whether 0.5 g and 2 g were sufficient to saturate the system or merely insufficient to perturb it remains an open question with direct implications for the interpretation of the null GWI effects on strategy measures.

      We thank the reviewer for this helpful point. We agree that gravitational processing may be non-linear around 1g, and we have revised the manuscript to acknowledge that more extreme gravitational manipulations may reveal effects that are not captured by 0.5g and 2g. In the present study, however, our aim was not to characterise the psychophysical function across different levels of altered gravity, but to test whether reasoning in a non-terrestrial gravitational context changes the effect of vestibular perturbation. We therefore tested whether hypogravity and hypergravity should be analysed separately by adding gravity level (0.5g vs 2g) to the mixed-effects models. Gravity level was not a significant predictor for any performance or strategy measure, and this was consistent with previous Virtual Tools work showing no reliable difference between hypo- and hypergravity effects. On this basis, we aggregated the two conditions as “altered gravity” for the main analyses (lines 248-253). We have now also clarified that this aggregation should not be interpreted as evidence that 0.5g and 2g are cognitively equivalent in all contexts, but rather that no reliable hypo/hyper distinction was detected in the present task and parameter range (lines 248-253, 462-463, 651-656).

      (R2.7) High-level reasoning is not defined with sufficient precision. The term "high-level reasoning" appears from the title onward and in the heading of the Study 1 results section (line 138), but it is never formally defined. The reader needs a clearer account of what distinguishes high-level physical reasoning from low-level sensorimotor prediction, and where the games used here fall along that continuum.

      We thank the reviewer for pointing this out and added a clear definition of high-level reasoning at the beginning of the Introduction section (lines 41-44).

      (R2.8) What specific physical competencies - ballistic trajectories, free-fall predictions, collision dynamics, frictional forces, inertial effects - are required across the game set? When describing the subset of games that drive key effects, this information is critical for evaluating whether effects are specific to gravity reasoning or to some other physical concept.

      The reviewer is correct that the Virtual Tools games require multiple physical competencies, including gravity-based prediction, collision dynamics, object support, containment, and the anticipation of object-to-object interactions. Importantly, most games do not isolate a single physical principle; rather, successful solutions typically require combining several physical predictions. For this reason, our aim was not to attribute each effect to one isolated physical primitive, such as ballistic motion or friction, but to test whether GVS effects were stronger in games whose solutions depended more heavily on gravity-based reasoning.

      To make this clearer, we revised the manuscript to state that the game set varied systematically in the extent to which gravity-related predictions were required for solution, and that this classification was based on piloting and previous computational modelling work in the lab (lines 112-116, 426-431, 588-594; Extended Data Table 1). This design is central to the logic of the study: if GVS produced a general disruption, effects should appear broadly across the task; if it affected gravity-dependent reasoning, effects should be most evident in games with higher gravity impact. We now clarify this point and avoid implying that the task isolates gravity from all other physical competencies.

      (R2.9) Performance measures are disconnected from underlying kinematics. The performance measures (success rate, number of attempts, time per attempt) are coarse, high-level summaries.

      We agree that success rate, number of attempts, and time per attempt are high-level measures. This is, however, aligned with the logic of the Virtual Tools task. Participants do not physically manipulate objects or perform complex reaching movements; they simply select a tool with one click and place it on the screen with another. The critical demand is therefore not manual dexterity or movement execution, but the ability to predict the physical cascade that will follow from a chosen tool and placement. For this reason, kinematic measures would not provide a direct index of the reasoning process targeted here. We have clarified this in the manuscript by emphasising that the task minimises motor demands and that performance depends primarily on tool selection and spatial placement, allowing us to focus on high-level physical reasoning rather than movement kinematics (lines 116-121).

      (R2.10) Time per attempt is used as a proxy for performance efficiency, yet participants received no instructions regarding speed, and different individuals may have adopted systematically different speed-accuracy trade-offs.

      We thank the reviewer for this helpful comment. We agree that time per attempt should not be interpreted as a straightforward performance-efficiency measure. Participants were told that each game had an overall time limit, but they were not instructed explicitly to respond as quickly as possible. Attempt duration could therefore reflect several processes, including deliberation, hesitation, task engagement, or individual speed–accuracy trade-offs, rather than better or worse reasoning. We have revised the manuscript accordingly: time per attempt is now treated cautiously as a secondary descriptive measure of attempt duration, rather than as a direct index of performance efficiency. We also clarified in the Methods that participants were given a time limit but no speed instruction, and in the Discussion that the time results should be interpreted with caution for precisely this reason (lines 121-125, 431-436, 558-560).

      (R2.11) It would be valuable to know whether time per attempt correlates with attempt number within a given game (which would indicate within-game learning) and whether mouse movement data - trajectory, velocity, hesitation - were recorded and could be analysed to provide more mechanistic insight into strategy formation.

      We thank the reviewer for this suggestion and agree with the reviewer that the insight into strategy formation (and hesitation in particular) could be examined more precisely, potentially using eye tracking to capture where participants look. We add a comment in our discussion (lines 434-436). We also included the correlation in our analyses (lines 183-186, 265-267, Extended Data Fig.1 and Fig.2).

      Reviewer #2 (Recommendations for the authors):

      (R2.12) Introduction, end of paragraph 2 (applied fields): The authors mention artificial intelligence and robotics as fields that could benefit from this work. Pedagogy and educational neuroscience are equally relevant: understanding how the brain conceptualizes gravity through embodied interaction could inform how physics is taught, and could motivate more sensorimotor-grounded instructional approaches.

      This is a great point! We have now added pedagogy and educational neuroscience as relevant fields, along with corresponding references (line 52).

      (R2.13) The gravitational acceleration is typeset as "ms<sup>2</sup>," which appears to conflate milliseconds-squared with the correct unit, m·s<sup>-2</up> (or m/s<sup>2</sup>). Please verify the superscript convention used throughout.

      We corrected the manuscript accordingly (line 78).

      (R2.14) When introducing GVS as the tool for gravitational manipulation, the authors should more explicitly clarify that GVS produces noise in the vestibular organ rather than a physical change in the gravitational environment. Readers familiar with space physiology will notice the asymmetry between this approach and paradigms involving true gravitational alteration (parabolic flight, centrifuges). The introduction should acknowledge this asymmetry upfront rather than leaving it to the Discussion.

      We agree that this distinction should be made explicit from the outset. We have therefore revised the Introduction to clarify that GVS does not physically alter the gravitational environment, as in parabolic flight or centrifuge paradigms, but instead introduces noise into vestibular signalling while participants remain in terrestrial gravity. This asymmetry is now acknowledged before the study rationale is presented, so that the scope of the manipulation is clear to readers from the beginning (lines 102-108).

      (R2.15) “covered by the same ethical approval than study 1" - should read "as study 1.”

      We corrected the manuscript accordingly (line 512).

      (R2.16) Line 422: Was the red text label the only cue indicating the gravity condition in the altered-gravity games? If so, this is an important detail with potential implications for how participants updated their expectations. Please clarify.

      We thank the reviewer for this comment. Yes, the red text label was the explicit cue indicating the gravity condition for each altered-gravity game. Participants were told before the task that the gravity condition would vary across games and that they should attend to the red sentence at the top of the screen to identify whether the current game was in hypo- or hypergravity. We have clarified this in the Methods, including that the visual label was presented for each game and served as the instruction cue for the gravitational context (lines 560-562).

      (R2.17) Lines 547-555 - GWI notation and coefficients: (a) Replace the subscript indices 1, 2, 3 in the equations with "low," "med," and "high" to improve readability and link the notation directly to the experimental conditions.

      We changed the manuscript accordingly (lines 697-706).

      (R2.18) Lines 547-555 - GWI notation and coefficients: (b) Justify the choice of weighting coefficients 1, 2, and 3. These are arbitrary integers that do not correspond to actual gravity levels or to the empirically estimated gravity impact scores used elsewhere in the paper. Either provide a principled rationale or demonstrate that the GWI result is robust across alternative plausible weightings.

      We agree with the reviewer that the original weighting coefficients of 1, 2, and 3 were not independently validated and therefore made the gravity-weighted index difficult to interpret. Rather than trying to justify these coefficients post hoc, we addressed the concern directly by removing the gravity-weighted index from the manuscript entirely. In the revised analysis, no numerical weights are assigned to the gravity-impact categories.

      Instead, we now calculate a Gravity Index separately for each measure and each gravity-impact level, and compare these indexes between Study 1 and Study 2 using Bayesian cumulative logit models. This approach preserves gravity impact as a categorical factor, avoids imposing an arbitrary linear weighting structure, and allows the cross-study comparison to be tested directly within each gravity-impact level. We have revised the Methods accordingly and describe this new analysis in detail (lines 140-147, 276-280, 658-666, 692-717). Thus, the revised manuscript no longer depends on the arbitrary GWI coefficients.

      (R2.19) Lines 547-555 - GWI notation and coefficients: (c) The composite expression in equation (1) - as currently typeset - reads as "Gravity minus Weighted Index"; please correct the formatting so it unambiguously reads as a gravity-weighted index.

      We corrected the manuscript accordingly (lines 702-706).

      (R2.20) P-values throughout the manuscript are reported without a leading zero (e.g., "p = .39") while Cohen's d values and other statistics include one. Please adopt a consistent convention; in line with APA 7 and most journal styles, all values between −1 and 1 should include a leading zero.

      Our statistics generally follow the APA 7th guidelines (https://apastyle.apa.org/instructional-aids/numbers-statistics-guide.pdf), which state: Put a zero before the decimal point when a number is less than 1 but the statistic can exceed 1. Do not use a zero before a decimal when the statistic cannot be greater than 1 (proportion, correlation, level of statistical significance). There was a formatting error in one of the tables (Table 3), which has now been corrected.

      (R2.21) The authors should either apply a correction for multiple comparisons to game-level t-tests or acknowledge this limitation explicitly and interpret game-level results as exploratory rather than confirmatory.

      See R1.1 and R2.1. We have revised the manuscript to clarify that each game was treated as a distinct physical-reasoning problem, and these were used to localize the games in which the stimulation effect was expressed, not to compare games with one another or to draw broad conclusions from isolated contrasts (and therefore, in this case, no multiple-comparison corrections are required; lines 637-640). That said, to address the reviewer’s concern directly and to strengthen our conclusions in the paper, we also added mixed-effect models using gravity impact level and stimulation type as fixed factors and participant and game as random effects for each study (lines 168-177, 202-207, 257-259, 301-303, 641-651, Tables 2 and 4).

      (R2.22) Discussion, circa line 274 (state of the body): The authors assert that their manipulation alters the body's gravitational state. This claim deserves qualification. Gravity perception results from distributed multisensory integration; the vestibular system is one contributor, but proprioception, vision, and visceral signals are equally involved. Moreover, altering gravity visually and disrupting vestibular signals does not approximate the full-body gravitational reweighting that occurs in real altered-gravity environments. This is not a fatal limitation, but the authors should acknowledge it more directly and discuss how their results might differ from findings in truly altered gravity contexts (e.g., parabolic flight or centrifuge experiments).

      We agree that our manipulation should not be described as equivalent to the full-body gravitational reweighting that occurs in real altered-gravity environments. GVS perturbs vestibular signalling while other sensory inputs remain anchored to 1g, whereas parabolic flight or centrifuge paradigms alter the gravitational context of the body more globally. We have revised the Discussion to make this distinction explicit and to qualify the scope of our claims accordingly (lines 353-359).

      At the same time, the distinction between full-body and partial gravitational manipulation does not undermine the logic of the present study. Parabolic flight or centrifuge paradigms would not be appropriate for the physical-reasoning task used here. Moreover, the parabolic-flight study by Kunavar et al. (2021), which the reviewer helpfully highlighted, also involved a mismatch between proprioceptive and vestibular signals. Taken together, these findings suggest that manipulating one important component of the gravity-processing system can still alter behaviour, while also leaving open the possibility that stronger or different effects may emerge when sensory conflict is reduced. We have incorporated this point in the revised Discussion (lines 356-370).

      (R2.23) Discussion, around line 290 (perturbing the body helps cognition): The authors make the interesting observation that vestibular perturbation can be beneficial in altered-gravity contexts. This resonates with a finding by Kunavar et al. (2021), who showed that restoring terrestrial gravity at the hand-environment interface while the body was immersed in real altered gravity during parabolic flight paradoxically improved performance - even though the gravitational context was incoherently distributed across body segments. That study suggests a broader principle: mismatch or perturbation within the sensorimotor system does not necessarily degrade cognition and may, in some circumstances, facilitate adaptation. The present results fit within this framework, and the authors should engage with this literature. The distinction between real and virtual gravitational manipulation is not merely semantic: even genuine immersion in microgravity during parabolic flight has been shown to be insufficient to update internal gravity representations unless paired with active sensorimotor engagement (Rannaud Monany et al., J Neurophysiol, 2022), which raises the question of whether a passive vestibular perturbation such as GVS can be expected to drive the kind of cognitive recalibration the authors propose.

      We thank the reviewer for drawing our attention to the work by Kunavar et al. (2021). We agree that this study is highly relevant, and we have now incorporated it into the Discussion (lines 360-364). We also agree that the work by Rannaud Monany et al. (2022) is important for interpreting our findings, and we now discuss it explicitly in the revised manuscript (lines 365-370).

      We have clarified our interpretation of the reviewer’s point about whether passive vestibular perturbation can drive cognitive recalibration. Our study did not involve passive exposure to GVS alone. Rather, participants reasoned about physical events while vestibular signalling was being perturbed. This is conceptually aligned with the conclusion of Rannaud Monany et al. (2022), where mere exposure to microgravity was not sufficient to update internal models, but exposure coupled with task-relevant motor imagery was. Similarly, in the present study, GVS was coupled with active physical reasoning, which we argue is critical for interpreting the observed effects.

      (R2.24) Discussion, circa line 295 (internal model of gravity): A natural reference point for the predictive coding discussion is McIntyre, Zago, Berthoz, and Lacquaniti (2001, Nature Neuroscience, "Does the brain model Newton's laws?"), which provides direct neurophysiological evidence for an internalized Newtonian physics engine in the brain. This paper should be cited here.

      We agree with the reviewer that this reference is relevant and added it to the manuscript (line 373).

      (R2.25) Discussion, circa line 352 (sensory modalities and adaptation): The proposal that vestibular perturbation accelerates the revision of gravity priors is intriguing. The authors should consider whether an analogous argument applies to visual and proprioceptive channels. They should also acknowledge that sustained adaptation to a novel environment requires not only a perturbation mechanism but also veridical, graded feedback so that the system can converge on an accurate updated model - a condition that is only partially met in the present paradigm.

      See R2.22. We revised the manuscript accordingly (lines 356-370).

      (R2.26) Discussion, closing paragraph (variability and adaptability): The authors raise the positive role of noise in living systems. This connects to a well-developed body of work on optimal movement variability (Stergiou and colleagues), which formalizes how neither too little nor too much variability is optimal for adaptive performance. This framework may help situate the authors' findings within a broader theoretical account of why controlled noise injection is beneficial in both biological and artificial systems. On the computational side, the same principle underlies several foundational algorithms: stochastic gradient descent (and its mini-batch variants) outperforms deterministic gradient descent in non-convex optimization landscapes; simulated annealing uses controlled random perturbation to escape local minima; Langevin dynamics leverages noise for efficient Bayesian sampling; and dropout regularization in deep neural networks injects noise during training to improve generalization. These are canonical examples where noise is not merely tolerated but functionally necessary - a point that strengthens the authors' broader theoretical message.

      We thank the reviewers for their suggestion and revised our manuscript accordingly (lines 385-390)

      Welling, M., & Teh, Y. W. (2011). Bayesian learning via stochastic gradient Langevin dynamics. In Proceedings of the 28th international conference on machine learning (ICML-11) (pp. 681-688).

      Stergiou, N., & Decker, L. M. (2011). Human movement variability, nonlinear dynamics, and pathology: is there a connection?. Human movement science, 30(5), 869–888. https://doi.org/10.1016/j.humov.2011.06.002

      Moss, F., Ward, L. M., & Sannita, W. G. (2004). Stochastic resonance and sensory information processing: a tutorial and review of application. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 115(2), 267–281. https://doi.org/10.1016/j.clinph.2003.09.014

      Sherman, S. O., Greenstein, M., Basner, M., Clark, T. K., & Anderson, A. P. (2023). Effects of additive sensory noise on cognition. Frontiers in human neuroscience, 17, 1092154. https://doi.org/10.3389/fnhum.2023.1092154

      Reviewer #3 (Public review):

      (R3.1) The main weakness of the manuscript is that its central conclusion is not strongly supported by the data. The key finding depends on a marginally significant cross-study comparison, whereas direct GVS-versus-Sham differences in Study 2 are minimal across aggregate measures. In addition, many game-level analyses involve a large number of uncorrected multiple comparisons, raising the possibility that some of the reported effects may reflect chance findings. The manuscript's most important metric, the Gravity-Weighted Index, was not preregistered and is exploratory in nature, yet it is treated as a primary basis for confirmatory conclusions.

      See R1.4 and R2.2. We moved away from the GWI method to apply a more robust comparison between the two studies. We agree that the original gravity-weighted index was not sufficiently justified because it combined the three gravity-impact levels, using numerical weights of 1,2, and 3. Although these weights followed the ordinal classification of the games, they were not independently validated and could therefore make the cross-study comparison difficult to interpret. To address this concern directly, we removed the gravity-weighted index entirely. In the revised manuscript, we instead use a Gravity Index that is calculated separately for each measure and each gravity-impact level, as the ratio between performance or strategy outcomes under GVS and Sham. This removes the arbitrary weighting scheme and provides a direct estimate of the stimulation effect within each gravity-impact category.

      We then compared these Gravity Indexes between Study 1 and Study 2 using mixed-effect models that include study and gravity-impact level as fixed factors, rather than collapsing across gravity-impact levels with our defined weights. We used the participant as a random factor. Because the indexes are bounded and showed clustering at structural values, we analysed them using Bayesian cumulative logit models, which are appropriate for bounded, non-normally distributed outcomes and allow the cross-study contrasts to be estimated without imposing a linear weighting structure. We also clarified how the gravity-impact categories were defined, based on piloting and previous computational modelling work in the lab, and report the classification of each game in Extended Data Table 1. These revisions directly address the reviewer’s concern by replacing the arbitrary weighted composite with a transparent, category-specific analysis of the GVS effect across terrestrial and altered-gravity contexts, in line with frameworks outlined by James et al. (2021), Li et al. (2023), Liu et al. (2017) (lines 140-147, 276-292, 307-317, 588-594, 658-666, 692-717, Table 5, Fig 5, Extended data Fig 3).

      James, N. T., Harrell Jr, F. E., & Shepherd, B. E. (2021). Bayesian cumulative probability models for continuous and mixed outcomes. arXiv preprint arXiv:2102.00330.

      Li, C., Tian, Y., Zeng, D., & Shepherd, B. E. (2023). Asymptotic properties for cumulative probability models for continuous outcomes. Mathematics, 11(24), 4896.

      Liu, Q., Shepherd, B. E., Li, C., & Harrell Jr, F. E. (2017). Modeling continuous response variables using ordinal regression. Statistics in medicine, 36(27), 4316-4335.

      Manuguerra, M., Heller, G. Z., & Ma, J. (2020). Continuous ordinal regression for analysis of visual analogue scales: The R package ordinalCont. Journal of Statistical Software, 96, 1-25.

      (R3.2) The cross-study comparison is also difficult to interpret because the two studies differ in participant samples, number of games, and partially in the stimulus set.

      We thank the reviewer for this comment. We have now clarified that the cross-study comparison was based on games that were common to Study 1 and Study 2 (lines 713-714). In addition, the indices were calculated in the same way in both studies, allowing us to compare the relative effect of GVS across the two experiments rather than relying on differences in raw performance between the two participant samples. We have emphasised this point in the revised manuscript (lines 712-715).

      (R3.3) Finally, the mechanistic claims in the Discussion-particularly those invoking predictive coding, stochastic resonance, or updating of internal gravity models-go well beyond what can be directly inferred from the present behavioral data.

      We took the reviewer’s advice, along with previous comments by the other reviewers and now added further findings in the literature, strengthening our claims in the Discussion (lines 356-370, 385-390).

      Reviewer #3 (Recommendations for the authors):

      (R3.4) Moderate the scope of the main conclusions. As currently presented, the results seem to support at most the possibility that vestibular gravity signals influence a limited subset of physical reasoning tasks under specific conditions, rather than physical reasoning more broadly. The manuscript would benefit from either a more cautious interpretation or additional supporting evidence from follow-up experiments.

      We revised the manuscript accordingly (lines 398-410, 423-436).

      (R3.5) Apply an appropriate correction for multiple comparisons to all game-level analyses (for example, FDR correction) and report clearly which effects remain significant after correction…A mixed-effects modeling approach, with the game treated as a random effect within participants, would also provide a more principled and statistically powerful analysis.

      See R1.1. We have revised the manuscript to clarify that each game was treated as a distinct physical-reasoning problem, and these were used to localize the games in which the stimulation effect was expressed, not to compare games with one another or to draw broad conclusions from isolated contrasts (and therefore, in this case, no multiple-comparison corrections are required; lines 637-640). That said, to address the reviewer’s concern directly and to strengthen our conclusions in the paper, we also added mixed-effect models using gravity impact level and stimulation type as fixed factors and participant and game as random effects for each study (lines 168-177, 202-207, 257-259, 301-303, 641-651, Tables 2 and 4).

      (R3.6) Provide a clearer justification for the classification of gravity dependence (low/medium/high) and for the weighting scheme used in the Gravity-Weighted Index. It would be helpful to report the quantitative criteria or validation procedure used to assign these categories, and to include sensitivity analyses showing whether the main findings are robust to alternative weighting schemes (for example, equal or binary weights). Treating gravity dependence as a continuous predictor could also strengthen the analysis.

      See R3.1. We abandoned the weighting system and use a Baysian cumulative logit model which does not imply any arbitrary weighting instead (lines 140-147, 276-292, 307-317, 588-594, 658-666, 692-717 , Table 5, Fig 5, Extended data Fig 3).

      (R3.7) Discuss the limitations of the cross-study comparison more explicitly. The two studies differ in participant samples, numbers of games, and partially in the task set, which makes the comparison difficult to interpret. At a minimum, the manuscript would benefit from a fuller comparison of participant demographics and other relevant characteristics.

      See R3.1. We used an indexing process which normalizes the data and isolates a relative effect. While raw scores could not be used to directly compare across the two studies, the use of a standardized scale allows valid comparisons between the experiments. We revised the manuscript accordingly (line 142-144).

      (R3.8) Ideally, future work should test Earth-gravity and altered-gravity conditions within the same participants.

      We agree that testing terrestrial- and altered-gravity conditions within the same participants would be a valuable design. However, we could not implement this approach for safety reasons, as it would have substantially increased each participant’s total exposure to GVS. The current design kept GVS exposure within acceptable safety limits, and we have now clarified this point in the revised manuscript and cited the work by Utz et al. related to safety limits (lines 619-620).

      Utz, K. S., Korluss, K., Schmidt, L., Rosenthal, A., Oppenländer, K., Keller, I., & Kerkhoff, G. (2011). Minor adverse effects of galvanic vestibular stimulation in persons with stroke and healthy individuals. Brain Injury, 25(11), 1058-1069.

      (R3.9) Address the lack of a direct manipulation check for GVS more explicitly. If any subjective reports related to dizziness, discomfort, or vestibular disturbance were collected, these could be reported as an indirect validation of the manipulation. If no such data are available, this limitation should be acknowledged clearly, and future work should consider including an independent validation measure such as postural stability.

      We thank the reviewer for raising this point. In our experiment, the maximum intensity was set at 1mA, which is well tolerated. There are no side effects with these GVS parameters. The stimulation protocol used a stochastic waveform, consisting of an alternating sum-of-sines voltage with dominant frequencies at 0.16, 0.32, 0.43, and 0.61 Hz, which has been used in previous studies, both in our and other labs. We have now added this information to the manuscript by citing the work of Zanchi et al. that used the same GVS protocol (lines 613-614).

      Zanchi, S., Cuturi, L. F., Sandini, G., Gori, M., & Ferrè, E. R. (2023). Vestibular contribution to spatial encoding. European Journal of Neuroscience, 58(9), 4034-4042.

      (R3.10) Reconsider the decision to collapse the 0.5g and 2g conditions into a single altered-gravity category. Even if the two conditions do not differ significantly in the current dataset, they are cognitively distinct manipulations, and combining them may obscure potentially meaningful interactions. Separate analyses, or at least explicit interaction tests, would strengthen the interpretation.

      We agree that 0.5g and 2g are cognitively distinct manipulations and that collapsing them should be justified statistically. We therefore conducted additional mixed-effects models including gravity level (0.5g vs 2g) as a factor (see responses R1.1 and R3.5). These analyses did not reveal significant differences between the two gravity levels for the relevant performance or strategy measures (lines 248-253). On this basis, we retained the aggregation of the 0.5g and 2g conditions as a single altered-gravity category in the main analyses.

      (R3.11) Clarify the interpretation of the tool-switching results. At present, the manuscript appears to invoke different post hoc explanations for different patterns of switching behavior, including increased exploration noise and reduced cognitive flexibility. The authors should either provide a more constrained interpretation or acknowledge more clearly that the current data do not distinguish between these possibilities.

      Increased switching can reflect a less stable or less efficient strategy, but in the context of a small number of attempts, indeed it may also reflect adaptive exploration or cognitive flexibility. The present data do not allow us to distinguish conclusively between these possibilities. We have therefore revised the Discussion to provide a more constrained interpretation and to acknowledge explicitly that the meaning of the switching-rate effects remains uncertain (lines 448-452).

      (R3.12) Expand the discussion of alternative explanations. In particular, the manuscript should address whether the observed effects could reflect non-vestibular influences of GVS, such as distraction, discomfort, changes in arousal, or anxiety-related effects, rather than a specific influence on physical reasoning.

      We thank the reviewer for this excellent suggestion. We agree that non-vestibular effects of GVS, including distraction, discomfort, arousal, or anxiety-related influences, should be considered. We have therefore revised the Discussion to acknowledge this possibility more explicitly. At the same time, the pattern of results argues against a primarily non-specific explanation: if GVS had mainly affected general attention, arousal, or discomfort, we would expect a broader impairment across games. Instead, the effects were limited to specific games and were related to the gravity relevance of the task. We have clarified that this selective pattern supports an interpretation in terms of gravity-dependent physical reasoning rather than a general consequence of stimulation, while still acknowledging the broader possible influences of GVS (lines 398-411).

      (R3.13) Acknowledge more explicitly that the Virtual Tools task depends on multiple cognitive processes in addition to physical reasoning, including visuospatial ability, executive function, and trial-and-error learning. A non-physical visuospatial control task would be helpful in future work for assessing the specificity of the reported effects.

      We agree with the reviewer and revised the manuscript accordingly (lines 437-438).

    1. eLife Assessment

      This important study shows that long-range somatostatin-expressing neurons in the ventrolateral periaqueductal grey that project to the rostral ventromedial medulla selectively suppress pain responses during conditioned fear. The evidence supporting these conclusions is exceptional, with methods spanning a novel cued fear-conditioned analgesia paradigm, cell-type-specific optogenetic activation and inhibition, anatomical circuit tracing, and in vivo spinal cord electrophysiology. These results will be of broad interest to systems and behavioral neuroscientists studying fear, pain, and descending pain-control circuitry.

    2. Reviewer #1 (Public review):

      [Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have addressed the comments raised in the previous round of review.]

      Summary:

      In the manuscript by Winke et al, the authors present evidence that fear-induced analgesia is mediated by somatostatin projection cells from the vlPAG to the RVM. This study uses a mouse model of fear-induced analgesia, and incorporates optogenetic circuit manipulation with behaviour and electrophysiology to gain a meaningful insight into a novel circuit involved in fear-induced analgesia.

      Strengths:

      (1) This is a well-constructed study with appropriate controls and analyses.

      (2) Alternative interpretations of the data are systematically considered and eliminated via rational experiments. The authors are commended for a nice piece of experimental work.

      (3) The vlPAG is a known region of pain modulation, and this study adds valuable insight to the circuit involved in fear-associated analgesia.

      Weaknesses:

      Only male mice are included in this study. [This has been explained and noted as a limitation.]

    3. Reviewer #2 (Public review):

      Summary:

      Wenke et al. investigated the role of vlPAG somatostatin-expressing neurons in the mediation of analgesia during defensive states. A newly developed paradigm of cued fear-conditioned analgesia, which consists of a combination of an auditory fear retrieval session and a pain test, was used to evaluate this cell population's contribution to fear-mediated analgesia. Optogenetic manipulation of vlPAG SST+ neurons modulated the responses to a nociceptive cue (Hot Plate) presented concomitantly with an aversively conditioned tone. At the same time, alterations in the freezing levels could be observed during optogenetic activation of vlPAG SST+ neurons. In order to disentangle the impact of these cells on analgesia from their impact on the expression of defensive behaviors, the authors performed electrophysiological recordings from the dorsal horn in the spinal cord of anesthetized mice. A vlPAG-RVM-DH pathway was identified to trigger nociceptive C-fibers upon optic activation of the RVM. Finally, pathway-specific activation of SST+ vlPAG-RVM neurons could abolish CS-induced analgesia.

      Strengths:

      The study addresses a relevant topic, that is, brainstem circuits for pain-modulatory mechanisms as part of defensive states evoked by threat. This is important because the circuit mechanisms underlying pain are still not fully understood, and defining molecular markers of cellular circuit substrates may support the identification of potential pharmaceutical targets in treating pain. The authors confirm a previous study in that a somatostatin-positive cellular population presents a crucial vlPAG circuit element mediating anti-nociceptive effects. Key novelty aspects of the present study are the demonstration that these neurons seem to play a role specifically in threat-induced analgesia. This was possible by the elegant design and application of a novel fear analgesia paradigm, combined with cell- and pathway-specific optogenetics.

    4. Reviewer #3 (Public review):

      Summary:

      Conditioned analgesia refers to the ability of a learned fear cue to suppress pain-related behavior and neural activity. Understudied, the authors developed a novel conditioned analgesia procedure in which a cue that had been paired or unpaired with shock was played while a hot plate increased temperature. Compared to several control conditions, the authors found increased latency to a nociceptive response (paw licking). The authors identified somatostatin neurons in the periaqueductal gray as a likely mediator of the behavior. They then showed that: (1) stimulating vlPAG-SST neurons blocked nociceptive response latency increases to the CS+, (2) stimulating vlPAG-SST neurons suppressed fear retrieval freezing, (3) stimulating vs. inhibiting vlPAG-SST neurons drove opposing modulation of c-fibers and Aδ-fibers, (4) direct-projecting vlPAG SST neurons modulate freezing while RVM-projecting vlPAG SST neurons modulate conditioned analgesia.

      Strengths:

      These experiments have many strengths. The behavioral assay is chief among them. The assay is robust and controls for confounding factors to reveal a repeatable effect of a shock-paired cue to delay nociceptive responding. The optogenetic experiments provide the correct level of temporal precision, given the authors' time-specific interest in cued responding. Combining neuronal manipulations with spinal recordings is particularly innovative, especially in the context of more behavioral neuroscience-based assays. All-in-all, I found this to be an exceptionally strong set of experiments.

      Weaknesses:

      No obvious weaknesses were identified by this reviewer.

    5. Author response:

      The following is the authors’ response to the original reviews.

      Note added by the authors, October 2026:

      In preparing the data deposit for the Version of Record we carried out a figure-level traceability audit of all 101 panels of the paper, recomputing every published statistic from the value table that produced the plot. This identified a small number of corrections, which are being made in the Version of Record.

      The substantive one is in Figure 6d. The CS × opsin interaction was reported as F(1,12) = 10.91, P = 0.0063; the correct value is F(1,12) = 19.87, P = 0.0008. The analysis as stored declared the repeated-measures factor on the opsin rather than on the CS, which is not the design: opsin is a between-subjects factor. The data, the group means and the effect size are unchanged, the corrected effect is stronger rather than weaker, and the conclusion — that photoactivation of the SST+ vlPAG–RVM pathway abolishes fear-induced analgesia — is unaffected.

      In Figure 1—figure supplement 3, panels e and g have been redrawn so that each bar equals the ratio of the cell counts printed on it; the eight percentages concerned have been corrected in the figure and in the legend. The cell counts themselves are unchanged, and no statistical test is applied to these panels.

      The remaining corrections are to reported details: the names of the statistical tests in the legends of Figures 1f and 4e, the number of cells in Figure 4d (n = 12) and in Figure 5f right (n = 8, and the JAX stock number of the Sst-IRES-Cre line (013044).

      The source data behind every quantitative panel of the paper are openly deposited in the Dryad Digital Repository: https://datadryad.org/dataset/doi:10.5061/dryad.63xsj3vj3

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In the manuscript by Winke et al, the authors present evidence that fear-induced analgesia is mediated by somatostatin projection cells from the vlPAG to the RVM. This study uses a mouse model of fear-induced analgesia, and incorporates optogenetic circuit manipulation with behaviour and electrophysiology to gain a meaningful insight into a novel circuit involved in fear-induced analgesia.

      Strengths:

      (1) This is a well-constructed study with appropriate controls and analyses.

      (2) Alternative interpretations of the data are systematically considered and eliminated via rational experiments. The authors are commended for a nice piece of experimental work.

      (3) The vlPAG is a known region of pain modulation, and this study adds valuable insight to the circuit involved in fear-associated analgesia.

      We are very thankful to the referee for these positive comments.

      Weaknesses:

      (1) Only male mice are included in this study.

      We thank the reviewer for this point. We used only males in this first study for practical reasons to work with a population as homogeneous as possible. However, taking sex differences in biological mechanisms into account, we included this restriction in the summary and discussion

      (2) Animals are excluded from analyses based on clearly defined criteria, but it is not clear how many mice were excluded from each group.

      We thank the reviewers for raising this point. As stated in the Methods, we applied strict inclusion criteria for mice undergoing the hot-plate test, specifically a discrimination index ≥ 0.4 and a conditioning index ≥ 0.3. Using these criteria, 23% of wild-type mice were excluded for failing to meet the discrimination criterion. In the transgenic groups, an average of 20% of mice failed to meet the learning criteria, and an additional 12% were excluded due to incorrect opsin injection or misplaced optic fiber placement.

      (3) The authors implement a pain sensitivity assay that involves a hot plate with progressively increasing temperature. The time to nociceptive responses is reported. Without reporting the actual temperature at which the mice respond, it makes it difficult to compare nociceptive responses to previously published work (which typically use a defined and static hotplate temperature).

      We thank the reviewer for this comment. We provided this information related to the actual temperature of the nociceptive response in the original manuscript in supplementary figures 1, 2 and 5.

      (4) The authors present evidence that inhibition of SST vlPAG cells enhances spinal nociceptive electrophysiological responses, but the corresponding pain sensitivity is not altered (Figure 2, CS- condition). The reason for the discrepancy between electrophysiological and behavioural responses is not clear.

      We believe this comment arises from a misunderstanding of our results. In our study, inhibiting SST+ vlPAG cells did not increase nociceptive electrophysiological responses. Instead, it decreased spinal nociceptive transmission, as evidenced by reduced nociceptive field potentials and WDR responses in Figure 4c,e. Consistent with this electrophysiological effect, photoinhibition of SST+ vlPAG cells also produced behavioral analgesia, as evidenced by increased nociceptive response latency in the hotplate test under both CS− and CS+ conditions (Figure 2f). Therefore, our electrophysiological and behavioral findings are not contradictory but instead support the conclusion that inhibiting SST+ vlPAG cells reduces pain sensitivity regardless of defensive state. We will revise the text to clarify this point.

      Reviewer #2 (Public review):

      Summary:

      Wenke et al. investigated the role of vlPAG somatostatin-expressing neurons in the mediation of analgesia during defensive states. A newly developed paradigm of cued fear-conditioned analgesia, which consists of a combination of an auditory fear retrieval session and a pain test, was used to evaluate this cell population's contribution to fear-mediated analgesia. Optogenetic manipulation of vlPAG SST+ neurons modulated the responses to a nociceptive cue (Hot Plate) presented concomitantly with an aversively conditioned tone. At the same time, alterations in the freezing levels could be observed during optogenetic activation of vlPAG SST+ neurons. In order to disentangle the impact of these cells on analgesia from their impact on the expression of defensive behaviors, the authors performed electrophysiological recordings from the dorsal horn in the spinal cord of anesthetized mice. A vlPAG-RVM-DH pathway was identified to trigger nociceptive C-fibers upon optic activation of the RVM. Finally, pathway-specific activation of SST+ vlPAG-RVM neurons could abolish CS-induced analgesia.

      Strengths:

      The study addresses a relevant topic, that is, brainstem circuits for pain-modulatory mechanisms as part of defensive states evoked by threat. This is important because the circuit mechanisms underlying pain are still not fully understood, and defining molecular markers of cellular circuit substrates may support the identification of potential pharmaceutical targets in treating pain. The authors confirm a previous study in that a somatostatin-positive cellular population presents a crucial vlPAG circuit element mediating anti-nociceptive effects. Key novelty aspects of the present study are the demonstration that these neurons seem to play a role specifically in threat-induced analgesia. This was possible by the elegant design and application of a novel fear analgesia paradigm, combined with cell- and pathway specific optogenetics.

      We thank the referee for such positive feedback.

      Weaknesses:

      Despite the convincing and rigorous experimental approach, the study leaves some interpretational room when it comes to the proposed circuit mechanism. This could either be addressed by additional experiments or by more discussion of alternative circuit layouts.

      Major Comments:

      (1) The paper by Zhang et al. (https://pubmed.ncbi.nlm.nih.gov/36641028/), which identified a role for vlPAG SOM+ neurons in mediating anti-nociception in neuropathic pain, needs to be referenced and its results discussed, if not reconciled. While functionally, both studies find an analgetic role of vlPAG SOM+ neurons projecting to the RVM, Zhang et al., using slice physiology, characterize those neurons as glutamatergic. In Figure 4E of Zhang et al. they find general (fear-independent) analgetic effects with PAG-RVM specificity by performing chemogenetic experiments.

      We thank the reviewer for highlighting this important point. We agree that the study by Zhang et al. is highly relevant and should be discussed in the revised manuscript. Their work shows that inhibiting vlPAG SST/SOM neurons with chemogenetic methods produces analgesia in a neuropathic pain model, and in our study, we similarly found that inhibiting SST+ vlPAG neurons increases hotplate response latency (Figure 2f), which aligns with an analgesic effect. Additionally, we observed that activating SST+ vlPAG neurons suppresses fear-conditioned analgesia.

      At the same time, there are important differences between the two studies that may explain the differences in interpretation. First, the behavioral paradigms are not identical. Zhang et al. used a hotplate protocol where animals were directly exposed to a nociceptive temperature, whereas in our study, we used a progressive temperature ramp and explicitly compared responses during a conditioned stimulus (CS+) and a non-conditioned control stimulus (CS−). These controls were important for us to distinguish fear-specific effects from more general effects related to stress, arousal, sensitization, or other non-associative processes.

      Second, the two studies differ in experimental context. Zhang et al. examined this circuit in a neuropathic pain model, whereas our study focused on acute nociceptive processing and fear-conditioned modulation of pain. We therefore believe that the apparent discrepancy might reflect differences in pain state and behavioral context, rather than a direct contradiction.

      Finally, Zhang et al. showed in slice recordings that SST+ vlPAG neurons provide excitatory input to RVM neurons. This is an important finding that we now address in the revised manuscript. At the same time, because the RVM contains heterogeneous neuronal populations with different projection targets and functions, these recordings alone do not prove that all recorded RVM neurons are part of the descending pathway controlling spinal nociception. Therefore, we have revised the Discussion to explicitly acknowledge Zhang et al. and to emphasize both the similarities and differences between the two studies.

      It can be argued that in addition to the two functionally distinct inhibitory SOM subtypes hypothesized by Winke et al., there is another, excitatory subpopulation. Also, the different experimental conditions (chronic vs. acute pain, non-threat vs. fearful cues/contexts may recruit different vlPAG SOM+ populations. All of this is conceivable, yet I wonder whether the contrasting findings could more parsimoniously be reconciled. The author's own results presented here in Supplementary Figure 3 suggests that SOM+ vlPAG cells are colocalizing with glutamate and thus could also be excitatory. In addition to this rather complementary piece of evidence, a more extensive characterization of vlPAG neurons using IHC and slice physiology would be needed to justify the unambiguous identification of their inhibitory nature.

      We thank the reviewer for this thoughtful comment. We agree that our current data do not support a definitive conclusion that all SST+ vlPAG neurons are inhibitory. As the reviewer notes, our Supplementary Figure 3 shows that SST+ vlPAG cells can also co-localize with glutamatergic markers, which is consistent with the possibility of cellular heterogeneity within this population. We also agree that different experimental conditions, such as chronic versus acute pain and non-threatening versus fear-related contexts, may activate different SST+ vlPAG subpopulations.

      Our intention was not to claim that SST+ vlPAG neurons constitute a uniform inhibitory population, but rather that SST+ cells are strongly represented among inhibitory neurons in the vlPAG. We agree, however, that more detailed characterization, including additional immunohistochemical analyses and slice physiology, is necessary to more definitively determine the neurotransmitter phenotype and functional connectivity of these neurons. We have therefore revised the text to temper our interpretation and to explicitly acknowledge the likely heterogeneity of SST+ vlPAG neurons, including the possibility of an excitatory subpopulation. We therefore modified the discussion accordingly:

      “Our results align with the parallel inhibition- excitation model, where inhibitory and excitatory cells form two distinct, parallel descending pathways for pain modulation.

      Indeed, previous research demonstrated the presence of an inhibitory pathway projecting throughout the PAG–RVM-spinal cord dorsal horn neuraxis. Our results complement this study by suggesting that one of these previously proposed parallel pathways is mediated by SST+ vlPAG cells and has a functional role in mediating analgesia. At the same time, our data indicate that vlPAG SST neurons are heterogeneous, with approximately one-third of these cells co-localizing with excitatory markers. Together with the recent observation that excitatory SST+ vlPAG neurons project to the RVM (Zhang et al., 2023), this raises the possibility that a subset of long-range SST+ vlPAG neurons contributes to an excitatory descending pathway within the PAG–RVM–spinal dorsal horn neuraxis. By contrast, local GABAergic SST+ vlPAG neurons may participate in local circuit mechanisms related to defensive-state expression, including freezing. Further anatomical and functional studies will be required to resolve these possibilities.”

      In the absence of a direct identification of these cells exclusively releasing GABA, an alternative explanation should be considered. What about looking at vlPAG SOM+ neurons as a putatively mixed bag of local, inhibitory interneurons and long-range, RVM-projecting excitatory cells? This model would then open up interesting questions as to the actual function of somatostatin as a modulator of vlPAG circuit activity and associated function, and from my perspective, would nicely fit into the view of PAG circuits as integrators of complex survival responses.

      We thank the reviewer for this insightful suggestion and agree that, in the absence of direct evidence that vlPAG SOM+/SST+ neurons are exclusively GABAergic, an alternative interpretation should be considered. In particular, we agree that this population may be heterogeneous and could include both local inhibitory interneurons and long-range excitatory neurons projecting to the RVM. We believe this is an important and constructive framework for interpreting our data, and we have revised the Discussion accordingly. In the revised text, we now explicitly acknowledge the likely heterogeneity of vlPAG SST+ neurons and discuss the possibility that distinct local and long-range SST+ subpopulations may contribute differently to defensive-state regulation and descending pain modulation. We agree with the reviewer on this point and have modified the discussion accordingly (see point above).

      (2) "Our data indicate that the optogenetic inhibition of SST+ vlPAG cells promotes analgesia irrespective of the animal's defensive state. In contrast, the optogenetic activation of long-range SST+ vlPAG cells that project to the rostral ventromedial medulla (RVM) abolishes the analgesia mediated by fear behavior." (lines 32-35). Consider toning down these conclusions, as contrasting activation with inhibition of two different (though overlapping) populations cannot be fully conclusive. Alternatively, a pathway-specific (vlPAG-RVM) inhibitory experiment could help to fully understand the circuit mechanism and verify the necessity of these neurons.

      We thank the reviewer for raising this point. We agree that inhibition of the entire SST+ vlPAG population and activation of the long-range SST+ vlPAG neurons projecting to the RVM population are not directly equivalent manipulations. Our conclusion was intended at the level of observed functional effects: inhibition of SST+ vlPAG neurons promotes analgesia regardless of the defensive state, while activating long-range SST+ vlPAG neurons projecting to the RVM suppresses fear-conditioned analgesia. This occurs regardless of whether the SST vlPAG neurons are excitatory or inhibitory. To address the excitatory or inhibitory nature of SST vlPAG neurons, we have revised the discussion to include a reference to the Zhang et al study.

      (3) Despite an overall very thorough reporting style, some information is missing from the manuscript:

      (a) In Figures 2d and f, what are the freezing levels during optogenetic manipulation? From Figure 3d, one can expect that freezing is inhibited during the hot plate test, which could bias the NC response towards shorter latencies.

      We thank the reviewer for this important comment. As shown in Figure 1e, we previously quantified freezing both at CS onset and at the time of the nociceptive response in the hot plate test. These analyses indicate that freezing levels at the time of the nociceptive response do not differ between the CS+ and CS− conditions. Therefore, the variation in hot plate response latency is unlikely to be due to differences in freezing at the time of response.

      We acknowledge, however, that freezing was not directly measured during optogenetic manipulation in this experiment. Based on the temporal profile of freezing shown in Figure 1e, we still consider it unlikely that the effect of optogenetic manipulation on nociceptive latency is mainly caused by a change in freezing behavior.

      (b) In Figure 5, the histological experiment showing the vlPAG-to-RVM pathway is presented by a qualitative image only. Here, some quantification would strengthen the finding.

      We thank the reviewer for this comment. The aim of the histological experiment in Figure 5 was to provide qualitative anatomical evidence that vlPAG projections reach the RVM and are positioned in close apposition to spinally projecting RVM neurons. We did not intend this experiment to serve as a quantitative characterization of connectivity. We agree that a more systematic quantification would be informative, but this would require additional dedicated experiments beyond the scope of the present manuscript.

      (c) In Figures 6 c and d "Consistently, activation of the SST+ vlPAG-RVM pathway during CFCA had no impact on CS-presentation, whereas the same manipulation performed during CS+ blocked the increase in NC response latency compared to GFP controls." (line 194-196). Is it possible that the NC response cannot be any lower than the one during CS-, thus constituting a floor effect?

      We are thankful to the reviewer for this important point. We agree with the reviewer that this is indeed a possibility. We have added a sentence in the discussion to acknowledge this limitation.“Another possibility is that our nociceptive test with a slow ramp of temperature induces a floor effect on nociceptive response latency, which may limit the detection of further decreases in latency under certain conditions.”

      (c) Connected to major point 1- this experiment is important for defining the circuit mode and therefore should be as convincing as possible. However, for the colocalization experiment in Supplementary Figure 3, the methodological description is missing and thus makes it hard to comprehend how this data set was generated (how many data points, etc.). The visual depiction of the results is non-standard and not easily graspable. Consider e.g., a Venn diagram.

      We apologize for this omission in the original manuscript. We have now provided this methodological information in the method section. We have now expanded the description of these data in the figure legend to ease the comprehension of the figure.

      Reviewer #3 (Public review):

      Summary:

      Conditioned analgesia refers to the ability of a learned fear cue to suppress pain-related behavior and neural activity. Understudied, the authors developed a novel conditioned analgesia procedure in which a cue that had been paired or unpaired with shock was played while a hot plate increased temperature. Compared to several control conditions, the authors found increased latency to a nociceptive response (paw licking). The authors identified somatostatin neurons in the periaqueductal gray as a likely mediator of the behavior. They then showed that: (1) stimulating vlPAG-SST neurons blocked nociceptive response latency increases to the CS+, (2) stimulating vlPAG-SST neurons suppressed fear retrieval freezing, (3) stimulating vs. inhibiting vlPAG-SST neurons drove opposing modulation of c-fibers and Aδfibers, (4) direct-projecting vlPAG SST neurons modulate freezing while RVM-projecting vlPAG SST neurons modulate conditioned analgesia.

      Strengths:

      These experiments have many strengths. The behavioral assay is chief among them. The assay is robust and controls for confounding factors to reveal a repeatable effect of a shock-paired cue to delay nociceptive responding. The optogenetic experiments provide the correct level of temporal precision, given the authors' time-specific interest in cued responding. Combining neuronal manipulations with spinal recordings is particularly innovative, especially in the context of more behavioral neuroscience-based assays. All-in-all, I found this to be an exceptionally strong set of experiments.

      Weaknesses:

      No obvious weaknesses were identified by this Reviewer.

      Recommendations for the authors:

      Comments from Reviewing Editor:

      Summary

      Three reviewers have assessed your manuscript on vlPAG somatostatin pathways contributing to conditioned analgesia. Conditioned analgesia refers to the ability of a learned fear cue to suppress pain-related behavior and neural activity. Understudied, the authors developed a novel conditioned analgesia procedure in which a cue that had been paired or unpaired with shock was played while a hot plate increased temperature. Compared to several control conditions, the authors found increased latency to a nociceptive response (paw licking). The authors identified somatostatin neurons in the periaqueductal gray as a likely mediator of the behavior. They then showed that: (1) stimulating vlPAG-SST neurons blocked nociceptive response latency increases to the CS+, (2) stimulating vlPAG-SST neurons suppressed fear retrieval freezing, (3) stimulating vs. inhibiting vlPAG-SST neurons drove opposing modulation of c-fibers and Aδ-fibers, (4) direct-projecting vlPAG SST neurons modulate freezing while RVM-projecting vlPAG SST neurons modulate conditioned analgesia.

      Strengths

      All three reviewers converged on multiple strengths. The assay developed was seen to be novel, rigorous, and included a variety of controls that convincingly demonstrated conditioned analgesia. Focusing on the ventrolateral periaqueductal gray, and more specifically on somatostatin-expressing cells, made prior sense, and the results more than justified this selection. Approaching the vlPAG and circuits with many converging methods provided further, compelling evidence for a role in conditioned analgesia.

      Weaknesses

      Specific weaknesses are described in the individual reviews. Generally, the following weaknesses were identified. The study only used male mice, a choice that should be better justified. Animals were reasonably excluded from analysis, but the final group ns for analyses were not always clear. Some statistical results lacked clarity. The relevance of these findings to prior work (particularly Zhang et al. 2023, Journal of Pain) was not always described. Relatedly, the results would be better contextualized by appreciating and describing the likely diversity of somatostatin functional types and projection types.

      Recommendations

      (1) Provide rationale for only using male mice, discuss the limitation of the exclusion of females, and note that male mice were the subjects in the abstract.

      Thank you for this recommendation, we have mentioned this information in the abstract and in the discussion. We have also mentioned the limitations of not including female mice in the abstract and the discussion of the revised manuscript.

      (2) Complete final report ns for each statistical analysis. If you have not already done so, please include full statistical reporting including exact p-values wherever possible alongside the summary statistics (test statistic and df) and, where appropriate, 95% confidence intervals. These should be reported for all key questions and not only when the p-value is less than 0.05 in the main manuscript.

      An extended table with all statistical tests and analysis for all figures has been provided in sup Table 1.

      (3) Include example videos of CFCA sessions, demonstrating optogenetic effects.

      We understand the editor’s request to include video material illustrating the behavioral responses. However, we would prefer not to include such videos in the manuscript, in accordance with our institution's guidelines and recommendations on the dissemination of animal experimentation footage. Importantly, all behavioral sessions were systematically video-recorded from both sides of the apparatus, allowing detailed offline analysis of the animals’ responses. These recordings were carefully examined by an experienced experimenter to assess nociceptive behaviors, including jumping responses and licking of the stimulated hindpaw. This procedure ensured a reliable and accurate evaluation of pain-related behavioral reactivity. While the videos themselves cannot be included in the manuscript for the reasons mentioned above, we believe that the behavioral scoring procedures described in the Methods section provide a clear and rigorous description of how these responses were assessed. In addition, Figure 1 includes an example image illustrating hindpaw licking behaviour, which is typically more subtle and more difficult to identify than jumping responses. We therefore believe that this visual example, together with the detailed description of the scoring procedure and the quantitative data provided, adequately supports the interpretation of the behavioural results.

      (4) Provide summary expression and ferrule placement figures.

      We thank the editor for this comment. We have now included schematic summaries of fiber placements for both SST and VIP mice used in this study, based on histological verification (Supplementary Figures 10 and 11). Representative images of viral expression are also provided (Figure 2a, Supplementary Figure 7b and f).

      (5) Detail how behavior judgments were made.

      We thank the editor for emphasizing this important methodological point. During all behavioral sessions, mice were video-recorded simultaneously from both sides of the apparatus, allowing a comprehensive and unobstructed view of the animals’ posture and movements throughout the experiment. These recordings were subsequently analyzed offline by an experienced experimenter trained to evaluate nociceptive behaviors. Pain-related behavioral responses were assessed based on well-established indicators of nociceptive reactivity. In particular, we quantified overt escape-like reactions such as jumping, which reflects a strong aversive response to the stimulus. In addition, we evaluated more localized nociceptive behaviors directed toward the stimulated limb, including licking of the hindpaw. These measures are commonly used in rodent pain assays and provide reliable behavioral readouts of nociceptive sensitivity. The combination of bilateral video recordings and expert behavioral scoring ensured that both subtle and robust nociceptive responses could be accurately detected and categorized during the analysis.

      (6) Provide the temperature at which nociceptive responses were initiated. Check grammar and references.

      The temperature at which nociceptive responses were initiated were originally reported in Supplementary Figure 1, 2 and 5.

      Reviewer #1 (Recommendations for the authors):

      (1) The authors use optogenetic manipulation of SST activity in the vlPAG to show that this cell type is involved in fear-induced analgesia. They include a valuable control to show that manipulation of another inhibitory cell type (VIP) also does not impact analgesia. It would be helpful to know the expression level of VIP cells in the vlPAG. Is this a predominant inhibitory projection cell in the vlPAG (besides SST)?

      We thank the reviewer for pointing this. While we did not quantify the expression level of VIP+ cells in the vlPAG in the present study, available data suggest that this population is relatively sparse compared to other inhibitory cell types. In particular, reference to the Allen brain atlas indicates that VIP gene expression in the vlPAG is limited and primarily localized around the fourth ventricle, within the lateral and ventrolateral PAG, rather than broadly distributed across the region. Consistent with this, we provide an example of viral expression in VIP-Cre mice in Supplementary Figure 7f, illustrating the restricted distribution of VIP+ neurons in the vlPAG. We have also provided a summary of ferrules placement for SST and VIP mice used in our study in Supplementary Figures 11 and 10, respectively.

      (2) The numbers of animals dropped from each experiment should be indicated - perhaps on the statistics table?

      We thank the reviewer for pointing this.

      As stated in the Methods, we applied strict inclusion criteria for mice undergoing the hot-plate test, specifically a discrimination index ≥ 0.4 and a conditioning index ≥ 0.3. Using these criteria, 23% of wild-type mice were excluded for failing to meet the discrimination criterion. In the transgenic groups, an average of 20% of mice failed to meet the learning criteria, and an additional 12% were excluded due to incorrect opsin injection or misplaced optic fiber placement.

      (3) Line 105: "...,which activity..." change to "..., whose activity..."

      Done

      Reviewer #2 (Recommendations for the authors):

      (1) Please also provide absolute temperature values of the nociceptive response threshold.

      The temperature at which nociceptive responses were initiated was originally reported in Supplementary Figure 1, 2 and 5.

      (2) It would be nice to see an example video of a CFCA session (with and without optogenetic manipulation).

      We understand the editor’s and reviewer’s request to include video material illustrating the behavioral responses. However, we would prefer not to include such videos in the manuscript, in accordance with our institution's guidelines and recommendations on the dissemination of animal experimentation footage. Importantly, all behavioral sessions were systematically video-recorded from both sides of the apparatus, allowing detailed offline analysis of the animals’ responses. These recordings were carefully examined by an experienced experimenter to assess nociceptive behaviors, including jumping responses and licking of the stimulated hindpaw. This procedure ensured a reliable and accurate evaluation of pain-related behavioral reactivity. While the videos themselves cannot be included in the manuscript for the reasons mentioned above, we believe that the behavioral scoring procedures described in the Methods section provide a clear and rigorous description of how these responses were assessed. In addition, Figure 1 includes an example image illustrating hindpaw licking behaviour, which is typically more subtle and more difficult to identify than jumping responses. We therefore believe that this visual example, together with the detailed description of the scoring procedure and the quantitative data provided, adequately supports the interpretation of the behavioural results.

      (3) Please provide a schematic summary of fiber placements and opsin expressions confirmed by histological examinations.

      We thank the reviewer for this comment. We have now included schematic summaries of fiber placements for both SST and VIP mice used in this study, based on histological verification (Supplementary Figures 10 and 11). Representative images of viral expression are also provided (Figure 2a, Supplementary Figure 7b and f).

      (4) "Valid nociception readout responses included jumping or licking the hindpaw." (Line 453). How was this evaluated- manually or automated, blinded etc.?

      We thank the reviewer for emphasizing this important methodological point. During all behavioral sessions, mice were video-recorded simultaneously from both sides of the apparatus, allowing a comprehensive and unobstructed view of the animals’ posture and movements throughout the experiment. These recordings were subsequently analyzed offline by an experienced experimenter trained to evaluate nociceptive behaviors. Pain-related behavioral responses were assessed based on well-established indicators of nociceptive reactivity. In particular, we quantified overt escape-like reactions such as jumping, which reflects a strong aversive response to the stimulus. In addition, we evaluated more localized nocifensive behaviors directed toward the stimulated limb, including licking of the hindpaw. These measures are commonly used in rodent pain assays and provide reliable behavioral readouts of nociceptive sensitivity.The combination of bilateral video recordings and expert behavioral scoring ensured that both subtle and robust nociceptive responses could be accurately detected and categorized during the analysis.

      (5) Line 226 REF33 doesn't seem to fit.

      The reference list has been updated. Related to this section in which we discuss the disinhibition mechanisms inducing nociception in chronic stress mice. We have cited the work of Samineni et al., 2015 (reference 15) and Tovote el al., (reference 23) both related to these disinhibition mechanisms.

      Full sentence for reference 33 (now 35): “Two independent previous studies found that long-range inhibitory inputs from the central medial amygdala contact inhibitory cells within the vlPAG, implicated in different roles: the modulation of fear behavior (23) and nociceptive transmission (35)”.

      Ref 35 - Yin, W. et al. A Central Amygdala–Ventrolateral Periaqueductal Gray Matter Pathway for Pain in a Mouse Model of Depression-like Behavior. Anesthesiology 132,1175–119 (2020)

      (6) Some minor language, semantic, and grammatical flaws.

      The manuscript has been evaluated for language, semantic and grammatical flaws

    1. eLife Assessment

      This valuable study builds a novel auditory-motor paradigm to investigate how the brain learns associations between movements and their auditory consequences. Solid evidence is provided for early ERPs (50-100 ms latency) reflecting violations of established key-pitch mappings. The study is of potential interest to researchers studying audition, sensorimotor integration, or predictive coding, but some statistical analyses, however, could still be improved.

    2. Reviewer #1 (Public review):

      Summary:

      Zhang et al. report on an ambitious study that investigates multiple aspects of the neural and behavioral underpinnings of auditory-motor surprisal in the context of an auditory-motor learning paradigm (piano keyboard). Using an intricate design comprising several sub-parts and control procedures, they report that early ERPs (50-100 ms latency) reflect violations of established key-pitch mappings.

      Strengths:

      The paradigm is quite intricate and at the same time addresses multiple aspects of auditory-motor learning and does so in an experimentally rigorous way.

      Weaknesses:

      In its current form, the analyses of the ERP data remain a weak point.

    3. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      Zhang et al. report on an ambitious study that investigates multiple aspects of the neural and behavioral underpinnings of auditory-motor surprisal in the context of an auditory-motor learning paradigm (piano keyboard). Using an intricate design comprising several sub-parts and control procedures, they report that early ERPs (50-100 ms latency) reflect violations of established key-pitch mappings.

      Strengths:

      This is a carefully devised and executed study. The paradigm is quite intricate and, at the same time, addresses multiple aspects of auditory-motor learning, and does so in a rigorous way.

      Weaknesses:

      Perhaps because of the exhaustive approach, it is sometimes difficult to follow which parts of the experimental design the results come from; there are some questions regarding appropriate statistical methods, the inclusion/treatment of musical background in participants, and the nature (latency & extent) of the identified neural components that detect auditory-motor violations.

      Reviewer #2 (Public review):

      Summary:

      Zhang et al. report an EEG study (n=18) of participants playing a keyboard where the correspondence between keys and pitches is varied to introduce sensory-motor mismatches (discrepancies between sensory inputs and expected sensory consequences of motor commands). They find that the auditory N100 amplitude is enhanced for the initial keystroke following a mapping switch but rapidly attenuates for subsequent keystrokes (showing rapid updating of the forward model), whereas the motor-related P50 amplitude only differentiates trained versus untrained mappings after 30 minutes of goal-directed practice (potentially showing timescales of inverse model updating). Using parallel univariate and mTRF decoding analyses, they conclude that forward models (mapping action to predicted sound) update almost instantly to track short-term context, while inverse models (mapping sound to motor commands) update slowly and require extended, targeted practice.

      Strengths

      (1) Methodological innovation:

      The study utilizes an interesting, continuous auditory-motor paradigm that moves beyond standard trial-by-trial oddball designs, offering a more ecologically valid measure of trial-to-trial adaptation.

      (2) Analytical elegance and rigor:

      The combination of traditional univariate ERP analyses with multivariate temporal response function (mTRF) decoding is elegant, allowing the authors to successfully dissociate overlapping auditory and motor variance streams.

      (3) The dissociation between the rapid adaptation of the N100 forward model and the slower adaptation of the P50 inverse model is interesting.

      Weaknesses

      (1) Confounded passive listening baseline:

      The passive listening control condition lacks an orthogonal behavioural task (e.g., an occasional oddball detection task). Active playing inherently necessitates focused attention on auditory feedback to monitor performance, whereas passive playback does not. The globally weaker stimulus-evoked pattern at electrode Fz during passive listening strongly suggests that the absence of an N100 effect in this condition may simply reflect a lower state of attention, rather than isolating the absence of a motor-driven forward prediction, in particular because the pure sensory suprisal was also enhanced for "firsts" notes, so this could also lead to stronger N1, but this effect may be masked.

      Here, our goal is to probe surprisal resulting from natural deviations of the expected sensorimotor map, and to test whether the observed response can be accounted for by the auditory components alone. In previous work, passive listening (without an explicit behavioral task) has been widely deemed sufficient to measure auditory surprisal (cf. Di Liberto 2020). Because the listener is not privy to the order of firsts and others within a session, any general attentional effects would be expected to influence all notes and therefore cannot explain the selective enhancement observed for “first” notes which follow a map change.

      (2) Overclaimed theoretical novelty:

      The conceptual framing leans excessively on the authors' specific "MirrorNet" framework, presenting foundational, decades-old tenets of the motor control literature (i.e., unsupervised exploration for forward models vs. supervised skill acquisition for inverse models; Wolpert, Jordan, both in the nineties) as their own novel "conjectures." This theory-heavy introduction obscures the paper's actual empirical contribution to the design and the interesting question regarding the distinct temporal adaptation scales of forward versus inverse models. I think some rewriting can improve the paper.

      Thank you for pointing this out. It was not our intention to present the work of Wolpert etc. as a novel conjecture. We substantially revised the Introduction to emphasize that our contribution builds upon that theoretical framework and is also a study of how this framework evolves over time. We have cited Wolpert in the Introduction, and have now integrated the forward and inverse pathways more clearly into our hypotheses about the different effects of long- versus short-term sensorimotor experience on the MirrorNet framework.

      (3) Misplaced surprisal terminology:

      In a similar vein, I find the use of the term "auditory-motor surprisal" more theoretical grandstanding than actually useful. The significance statement claims to "extend this principle from sensory processing" but in fact, the concept of sensory motor unexpectedness is again a staple of the forward motor literature. Moreover, nowhere in the paper do they actually estimate sensorimotor surprisal. While the authors compute surprisal for their auditory baseline using IDyOM, their central sensorimotor analysis relies entirely on a simple categorical mismatch (first vs. subsequent keystrokes). The phenomenon can equally be referred to by its established nomenclature-"sensorimotor mismatch" or "sensory motor unexpectedness".

      In the revised Introduction, we added a discussion of the relationship between surprisal and contextual predictions to clarify our terminology. In practice, the terms auditory-motor surprisal and sensorimotor mismatch refer to closely related phenomena. However, we chose the term surprisal because it emphasizes that prediction violations depend not only on the overall frequency of an event, but also on the context in which it occurs. In the surprisal literature, an event can be unexpected because it violates predictions generated from recent context, or long-term statistical regularities, or prior experience, even if the event itself is not objectively rare.

      This distinction is important for our paradigm. A given note may occur frequently throughout the experiment and therefore is not rare in an absolute sense. Nevertheless, the same note may be highly surprising if it occurs immediately after a key-pitch map change, thus violating the participant's sensorimotor predictions. We therefore use the term surprisal to emphasize the role of contextual expectations rather than simple deviance or rarity.

      We finally agree that, in the absence of an auditory-motor equivalent of models such as IDyOM, our study does not provide a continuous estimate of surprisal. Instead, we use the distinction between first and keystrokes as a categorical approximation of high- and low-surprisal events. Developing computational models that estimate continuous auditory-motor surprisal from the preceding sensorimotor context would be an interesting direction for future work, and we have added a note concerning this in the Conclusion.

      (4) Incremental conceptual advance regarding the N100:

      The paper frames the N100 finding as a major discovery, but as far as I know, the attenuation of the auditory N1 to self-generated sounds via accurate motor prediction-and its enhancement during sensorimotor mismatch - is one of the most heavily documented phenomena in the auditory-motor literature (e.g. Timm et al., 2013; Bendixen et al, 2012; 2013). As far as I'm concerned, the authors should clarify that the novelty lies in the novel, elegant design that provides a new way to correct for non-sensory-specific motor-induced attenuation, and characterizing the distinct adaptation timescales of forward versus inverse models -- not in demonstrating N100 modulation by sensorimotor mismatch, which is well-documented, AFAIC.

      We agree now that the main finding of the paper is not discovering N100 as a signature of sensorimotor mismatch and we have revised the Introduction as discussed above. We now instead emphasize how it evolves over time and connect it to behavioural studies of sensorimotor perturbation where a behavioural adaptation is observed after one trial. We also incorporated the suggested literature on the N100 in the Introduction and Discussion because it shows that this neural signal has been previously seen in sensorimotor mismatch studies.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      As very general feedback, I would encourage the authors to revise the manuscript so that it is much clearer which results come from which part of the experiment. For example, it took me a while to figure out that the ERP results in Figure 3 came solely from the 'Variable map playing' parts of the study (and perhaps only from the Pre-test phase?).

      To improve clarity, we have rearranged the order of the figures and the respective text to organize the article into three main parts: the Variable Playing Task (Experiment I), the Training (Experiment II), and the TRF analysis (Experiment III). The paradigm panels are now closer to the Figure explaining their results. The Figure outlining how these tasks are carried out (previously Figure 2D) during one experimental session has been relegated to the supplementary materials.

      Major:

      (1) Statistics

      (a) I couldn't shake the impression that the authors are treating the statistical analyses a bit too casually. Some analyses use parametric (ANOVA, t-test), others non-parametric tests (Wilcoxon) or bootstrapping, without explicitly reasoning for one or the other; there are also some examples where I believe no correction for multiple comparisons is applied, but should be applied (e.g., tests in Figure 4?).

      We have added information in the Methods section to clarify the statistical choices. Here is a summary of our reasoning:

      Bootstrapping (to find the difference between first and others): this was to account for the imbalance in class sizes and is more conservative than parametric alternatives. That being said, parametric tests do converge on the same result (Figure 6C, main effect of keystroke type in the 2-way within-subjects

      ANOVA).

      For Figure 4, we have replaced the pairwise tests with a more robust linear mixed-effects model to capture the relationship between the number of previous keystrokes and amplitude. This avoids multiple pairwise comparisons between conditions. However, the conclusions of the figure remain unchanged: the amplitude difference increases with the number of previous keystrokes.

      Wilcoxon tests (subject-wise differences): With N = 18 participants, we do not meet the conventional threshold for normality (n > 30). The Wilcoxon test was therefore the appropriate choice.

      Within-subjects ANOVA (training and first vs. others): A two-way within-subjects ANOVA was used when comparing two conditions. Within-subjects designs with N = 18 do not violate ANOVA assumptions in the same way between-subjects designs would, since the critical assumption is the sphericity of difference scores rather than marginal normality.

      (b) Relatedly, I am not sure it is ok to run uncorrected statistical tests to identify 'time regions of interest', and to then zoom in on these and run additional statistical tests on them; this seems conspicuously like double dipping?

      We did the uncorrected tests as an exploratory analysis, but we would have arrived at the same result if we chose timepoints of interest based on the literature (P50 and P100 were both well-documented; P250+ also potentially could have had an effect based on the literature (Lutz et al., 2013 for example), but in our experiment we did not find it).

      (c) Comparing firsts versus other keystrokes: I wonder whether the bootstrapping procedure is necessary? In the MMN literature, this imbalance (oddball vs. standard) has not been an issue, as far as I am aware (or it has not been addressed?); I can follow the authors' reasoning for addressing this imbalance, but would feel more comfortable with the bootstrapping procedure if the authors can show that there is only a quantitative, but not a qualitative difference between statistics with bootstrapping vs. without bootstrapping (i.e. having a large imbalance between firsts and others keystrokes).

      While bootstrapping is less commonly used in some ERP studies, we chose this approach because it provides a robust estimate under class imbalance between conditions, as is typical in oddball-like paradigms. The effect of firsts versus others keystrokes remains significant under standard parametric analyses, consistent with the main effect observed in the two-way ANOVA (Figure 6C).

      (d) In addition, is it really correct to derive a null distribution only from the other keystrokes, as opposed to from both firsts and other keystrokes?

      Our procedure defines a null distribution based on differences between randomly sampled other keystrokes. This allows us to quantify whether the observed amplitude difference between first and other keystrokes exceeds what would be expected from differences within the other condition alone. Under the null hypothesis, the first–other difference does not differ from the distribution of differences obtained by comparing randomly selected subsets of other keystrokes of equal size.

      (2) Latency and duration of ERP differences

      I was rather surprised by how early the effects appear to be. Coming back to the MMN paradigm: here, the latency of the peak MMN for oddballs is typically in the ~150-250 ms range, i.e. in P2 territory. Granted, the effect can be initiated in the N1 component range, but then it persists through P2; in the current paradigm, the effect is very selective to the N1 (or even the P50 latency for Pre- vs. Post-training comparisons, which puts this effect squarely in the primary auditory cortex). I would encourage the authors to explore this apparent discrepancy with respect to the MMN latency a bit more; is this related to passive vs. active paradigms? But why does the effect not persist and is seemingly restricted to a brief time window? Presumably, the auditory-motor mismatch, once it is detected, will have 'downstream' effects?

      The early N100 effect has been reported multiple times in previous literature so we don't find this result surprising. It's possible that there are downstream effects that we're not seeing here. For example, we show in Figure S13 that the playing ERP is likely a linear sum of auditory and motor signals. The motor ERP also peaks at around 150, so in theory it could obscure the classic MMN. This point was mentioned in the Discussion (line 290).

      (3) Musical background of participants

      The authors state that some participants had more than 10 years of formal musical training, but that "none of the musically trained participants played piano as their primary instrument". How many of those nevertheless played piano? And how did this affect their performance? It would have been much cleaner to restrict participants to non-musicians...

      I am not convinced that performance in the training task is an appropriate proxy for musicianship (as the authors suppose on page 10). In general, I think the consideration of musicianship in the study is by far the weakest and should probably be left out.

      That being said, it is unfortunate that not more information on participants' background is provided, and that the authors did not exclude musicians, as this likely also has an impact on the results, since they presumably have had a stronger 'natural' key-pitch mapping than non-musicians.

      Participants did fill out a questionnaire adapted from the Goldsmiths Musical Sophistication Index previously used in our laboratory’s other studies.We found no correlation with the GSI general score, nor the subscores. We have added the questionnaire results in the supplementary materials and the correlation plots for the overall musical sophistication score and the perceptual abilities score (the subscale most closely associated with the demands of our task). Nevertheless, for our use case, we do not believe that the questionnaire is more relevant for predicting sensorimotor learning abilities than the actual performance during training. For example, aspects of emotion and social engagement in music are also measured, which we don’t find to be relevant to musical learning abilities, and perceptual abilities are self-reported.

      (4) How were peak amplitudes determined? (They should not be determined by simply picking the maximum, as this is susceptible to outliers, but instead as an average over a pre-defined time window.) In general, the description of methods would benefit from being more detailed.

      We indeed computed the peak response as the maximum over a 40 ms window. However, given the large number of trials, and that the signal was already low-pass filtered to 30 Hz, corresponding to a ~33 ms cycle, it is unlikely that the estimate is significantly affected by transient noise or spurious peak fluctuations. The concern about outliers would be more relevant for single-trial data.

      (5) Figure 3A and 6A 'Pre-training ERP' seem to be identical; why is that? Shouldn't Figure 3A essentially show the average of Figure 6A 'Pre-test ERP' and 6A 'Post-test ERP'?

      Yes, it is indeed identical. Figure 3A is the variable map playing without the influence of any other tasks, therefore it’s the pre-training ERP.

      (6) I had trouble following the results in Figure 4B/D. Why does 4B only go as far as 3 previous keystrokes, but 4D goes as far as 7? Maybe I am misunderstanding something here?

      We showed only the contexts for which there were more than 300 trials, which we noted in the Methods section. There are fewer firsts in general, and fewer trials for higher numbers of keystrokes in the context. Therefore the 300 trial cutoff allows us to go up to 3 previous keystrokes for Figure 4B, but 7 previous keystrokes for Figure 4D.

      (7) In Figures 3E and 3F, eyeballing and comparing the null and listen vs. null distributions (3E) with the First vs. Other distributions (3F), I had trouble wrapping my head around how the ones in 3F can be highly significantly different (p<0.001), while the others in 3E are not. (See also my question above re the calculation of the null distribution.)

      Regarding Figure 3E, as detailed in the Methods section, the comparison between playing and listening was performed using a permutation test, whereas the p-values for comparisons between playing and null or listening and null were computed empirically as the proportion of values in the null distribution that exceeded the observed statistic. This is because playing and listening are both empirical (non-null) distributions, making a direct permutation test more appropriate for their comparison.

      (8) Please clarify whether or not the decoders were compared to all keystrokes (firsts and others)?

      The decoders were compared to all keystrokes in the main text (Figure 7), but a sized-match sample of firsts and others yielded the same conclusion (Figure S12).

      (9) Last paragraph of Disentangling auditory...

      If I understand it correctly, then neither ANOVA revealed an interaction; yet, the authors computed post-hoc pairwise comparisons nevertheless?

      There is an interaction for P50 according to the ANOVA (Figure 6C). We followed up with post-hoc comparisons to investigate whether this interaction is driven by a particular key-pitch map.

      (10) Figure S9: More information is needed re the TRF decoder; why such a short time window (as opposed to (showing) the ERP epoch -.2 to .5 s)? What does it mean for the decoder to have its peak at 0 s?

      Since we used an inverse TRF formulation, the target reconstruction corresponds to a sparse impulse vector with value 1 at keystroke onsets and 0 elsewhere. As a result, only the temporal location at t=0 is of interest, with the expectation that the reconstruction should be flat outside this point. As a sanity check, we verified that averaging across onset-aligned windows reveals a clear peak at t=0, confirming that the model captures the expected temporal alignment between keystrokes and reconstructed responses.

      Minor:

      (1) Please include line numbers in the revised document to facilitate the review of the manuscript.

      (2) Baseline period: either baseline correct to 100 ms, or show the entire 200 ms baseline period in the figures.

      No baseline correction was applied to the ERP signals. Instead, data were re-referenced to the mastoids and band-pass filtered (1–30 Hz) to reduce slow drifts and high-frequency noise. As shown in Figure S5, the pre-stimulus baseline period does not exhibit systematic drift across conditions, consistent with stable baseline activity following preprocessing.

      (3) I was surprised to read that the authors used Sennheiser 650 headphones - surely they introduce significant electrical interference?!

      We have previously used headphones in EEG experiments in our lab without issues (cf. Cantisani 2023, Di Liberto 2020). In preprocessing, a notch filter at 50 Hz is applied to remove line noise, and ERP analyses are further low-pass filtered at 30 Hz to isolate the relevant evoked response components.

      (4) ICA should be run on continuous data to avoid situations where an eyeblink cannot be accurately identified (and removed) because it's cut off by the epoch (and this way it would remain in the epoch and contribute noise).

      ICA was indeed run on the continuous data (10 minutes for each task). We clarified this in the Methods section.

      (5) The supplementary video was not available.

      We have let the eLife team know about this.

      (6) Figure 2F: I believe the x-axis tick marks should read Block1 and Block2, instead of Pre and Post? Please clarify (also in the corresponding text) whether this is averaged across Pre-training and Post-training sessions, or only for one of them?

      Thank you for pointing out the error, it should indeed read block 1 and block 2, and has been updated. Both take place during the 30-minute training session, which consists of 2 blocks.

      (7) Figure 3: It wasn't clear to me how only significant channels are shown.

      We applied a mask so that only the significant channels are coloured according to amplitude difference. Non-significant channels are shown in white. The explanation for this is now found in Results > Experiment II > Effects of targeted skill acquisition training.

      (8) For the decoder analyses, how did the authors determine that investigating/using the same electrodes for auditory vs. motor ERPs is appropriate? I am aware that ERPs evoked by the auditory cortex are maximal at fronto-central electrodes; is the same the case for motor ERPs?

      We did not perform any pre-selection of channels for the decoder analyses. All channels were included in model training and prediction, allowing the decoder to learn data-driven spatial weights for reconstruction without imposing priors about channel relevance for auditory or motor processing.

      (9) Figure 4A/C: The stems represent keystrokes during the Variable map Pre/Post test? This indicates that this particular participant often only played 3 notes?

      Participants occasionally paused after producing short sequences of 3–5 notes. This behavior varied across individuals, with some producing near-continuous sequences with brief (< 1 s) pauses, and others inserting longer pauses (2–3 s) between melodic segments. Participants were instructed to generate short, varied melodic motifs and to avoid repeating identical sequences.

      (10) Figure 5E: Were these based on size-matched comparisons, i.e. similar to the method the authors applied to the other analyses comparing first vs. other keystrokes?

      The figure shows the means without size matching, but using size-matched comparisons doesn’t change the conclusions of the figures; we added this analysis in a supplementary figure.

      (11) Figure 6C and 6G: there seem to only be 16 and 17 datapoints, respectively?

      Figure 6C: We double-checked in the code and SVG file and there are indeed 18 subjects and 18 points.

      Some points are not clearly visible in the figure due to very close overlapping.

      Figure 6G: We unintentionally excluded one subject from the plot by setting the x-axis limit to between 2 and 4 on the training score. We have now adjusted the plot so that all subjects are visible.

      (12) Figure 6C: Why an ROI here, but not elsewhere?

      The ROI selection was based on prior ERP studies reporting robust auditory responses over central electrodes and motor-related error responses over frontal midline electrodes (Gehring 1993, Lutz 2013).

    1. eLife Assessment

      This fundamental study examines the benefits of spatial cognition in a wild population of mountain chickadees. Using robust genetic analyses and experimental design, the authors show with compelling evidence that females seeking out extra-pair copulations prefer males with strong spatial cognition, and that these males have a reproductive advantage over other males. This work will be of broad interest to evolutionary and behavioural biologists.

    2. Reviewer #1 (Public review):

      This study demonstrates sexual selection on spatial cognition via extra-pair paternity in socially monogamous, non-migratory mountain chickadees (Poecile gambeli). Males rely on spatial memory to recover food caches, with abilities linked to survival, longevity, and heritability. In a wild population, authors quantified extra-pair young (EPY) and compared the cognition of extra-pair sires to cuckolded social males using feeder-array assays. Better-performing males sired more EPY and heavier offspring in their nests; extra-pair sires significantly outperformed social males. Results support the good-genes hypothesis: females gain indirect genetic benefits via EPY from cognitively superior mates.

    3. Reviewer #2 (Public review):

      Summary:

      In this study, the authors ask whether spatial cognition is under sexual selection in mountain chickadees. To do so, the authors examined a large dataset that includes a) spatial cognition data for both males and females (obtained via use of a clever RFID-based feeder system) and b) social and extra-pair paternity nesting data. As predicted, males with higher spatial cognition sired more extra-pair offspring, and extra-pair sires had, on average, higher spatial cognition scores than the males they cuckolded. Interestingly, females with lower spatial cognition scores were more likely to seek extra-pair copulations, potentially to compensate for their own low spatial cognition. Surprisingly, there was no difference in spatial cognition scores between males that sired their own offspring and those that lost paternity at the nest. Also surprising was the fact that there were no differences in patterns of extra-pair paternity and spatial cognition between high- and low-elevation sites. The latter is particularly surprising in that spatial cognition should be under stronger selection at the high elevation site. Overall, this is a fascinating study that demonstrates that spatial cognition - a trait under natural selection as it directly impacts winter foraging and survival behaviour -is also under sexual selection.

      Strengths:

      The authors have a robust dataset (n = 732 offspring sampled over 3 years), high-quality spatial cognition data collected with a procedure that has been well-honed over the years, and couple the data with solid statistical procedures that address many potential covariates and potentially confounding factors. In addition, the authors are careful in the discussion to elaborate on the many potential alternative explanations from the results and questions that are likely to arise in the minds of readers (e.g., how are females assessing male spatial ability?)

    4. Reviewer #3 (Public review):

      Summary:

      The authors presented evidence that spatial cognition in this population is under sexual selection, with extra-pair males, primarily chosen by the females, having better spatial cognition than males they cuckolded and males with better spatial cognition having more extra-pair young.

      Strengths:

      This cognitive ecology study was conducted on a well-known long-term study population of free-ranging mountain chickadees. This strong base, alongside a thorough study design and extensive statistical analyses, enabled the authors to address research questions that few other labs can address, making this a potentially powerful study of broad general interest.

    5. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      This manuscript presents compelling evidence from a wild chickadee population linking heritable spatial cognition to extra-pair paternity success, supporting sexual selection via good genes in a food-caching species. The integration of RFID cognition tests with ddRAD paternity assignment is methodologically strong and timely for behavioral ecology, though causal mechanisms and confounds warrant clarification.

      Overall, a major revision of the manuscript is recommended, addressing the points below.

      (1) Confirmation of manipulation and treatment effects.

      The central claim hinges on spatial cognition driving EP siring, but direct evidence that cognition predicts observed copulations (vs. post-copulatory mechanisms) is absent. While territories do not cluster by performance (Figure S4), quantify male aggression/movement data during fertile periods to rule out intrusion-based EPP. The authors should provide metrics like nearestneighbor distances for EP sires or playback responses linking cognition to dominance, as in prior chickadee work. Without this, causal female preference remains correlational.

      We do provide the mean distance to all known EP sires in Figure S2, in addition to the known neighbors coded by cognition and EPP (Figures S3 and S4). Unfortunately, we do not have data with playbacks for this set of individuals, though we have shown that males respond to threats from high or low elevation birds equally, which provides a potential proxy for cognitive performance and dominance (e.g., high elevation birds perform better on the task than low elevation birds, and low elevation birds are dominant/,more aggressive than high elevation birds; Kozlovsky et al. 2014, Beh Ecol and Sociobiol; Branch et al. 2015, Behaviour; Branch & Pravosudov 2016, Ethology).

      As mentioned in discussion of the manuscript, we do not see a significant relationship between dominance and task performance in our system (Heinen et al 2021) – this is also true for the closely-related black-capped chickadees (Robayo Noguera et al 2025). This is not to say that social dominance status does not affect female preference or choice, but that data is outside the scope of this work. We would like to highlight that we’ve shown high repeatability in individual task performance and shown a strong gene c basis for spa al learning and memory performance (Sonnenberg et al. 2019; Branch et al 2022; Semenov 2024, Current Biology). While social dominance status is a rela ve trait and known to change with me and age (eg adults are dominant to juveniles).

      From discussion:

      “Furthermore, while higher social dominance status is associated with access to better territories in chickadees (86), we see no association with dominance status and spatial cognitive abilities in our system (46) or closely related black-capped chickadees (81). While it is possible that our results reflect some unmeasured trait(s) linked to spatial cognitive ability, such as boldness, aggression, or increased exploratory behavior (e.g., 32), previous data from our system do not support these additional alternative hypotheses. Chickadees with enhanced spatial cognitive abilities are not more aggressive (78) nor do they show higher rates of exploratory behavior (e.g., higher space use) (79).”

      We agree that the results we present are correlational. A causal relationship is outside the scope of this research, as we cannot manipulate cognition in the wild and chickadees will not breed in captivity. The strength of our approach lies in multi-year data collection and the ability to compare among the social and extra-pair males, as well as using what we know about the system and species to rule out additional explanations, including age.

      (2) Female cognition-EPY link inconsistency.

      Poor female cognition predicts more EPY (first-20-trials: offspring-level χ<sup>2</sup>=6.21, P=0.013; nests: χ<sup>2</sup>=6.79, P=0.009), but not for full-task (P>0.5). The authors should discuss why (e.g., learning speed vs. memory stability) and add exploratory correlations (female errors vs. EPY proportion). They should soften claims in the Discussion section of "female-driven" without consistent support and should frame this as a hypothesis.

      Absolutely, we agree that this is an interesting nuance to the results regarding female cognition. While number of errors on the first 20 trials and across the entire task are highly correlated, this is not a 1-to-1 relationship (Figure S7a). We suggest that performance in the first 20 trials reflects memory acquisition, while performance across the full task reflects memory retention and recall (Sonnenberg et al. 2019) – both critical components of spatial learning and memory for food-caching birds.

      Previous work has shown that performance in the first 20 trials and across the entire task is linked to survival (Sonnenberg et al. 2010) and longer lifespan (Welklin et al. 2024) – meaning both metrics reflect fitness. In the discussion, we suggest females may be compensating for their poorer abilities by seeking EPY, but this is speculative, as we do not have data beyond the correlative relationship we report and cannot provide more insight to the difference between the first-20 and entire task. This said, it is not only poor performing females that have EPY in the nest and we included the female/mother identity as a random factor in our models (see Statistical analysis section of Methods). Certainly, further investigation into female task performance and mate choice is warranted.

      Edit to MS:

      In the discussion we have edited the sentence to be more specific to our findings:

      “Indeed, females with poorer spatial memory acquisition (made more errors on the first 20 trials) had more extra-pair young in their nests, while social male cognitive performance did not predict EPY in the nest.”

      (3) Cognitive task sensitivity and validity.

      Mean errors aggregate learning curves effectively, but single feeder-assignment (nonpreferred) confounds neophobia/motivation with spatial ability. The authors should report trial-by-trial improvements (Figure S7 subset) or criterion-to-learn metrics. Justify excluding high-error birds (<3 mean); sensitivity analysis needed to check bias toward high performers.

      We respectfully disagree that our task lacks sensitivity or validity. The criterion-to-learn or trials-to-criterion metric has been criticized for decades and should not be used in studies comparing individual variation, in our opinion (e.g., Pravosudov 2022, Current Opinion in Behavioral Science; Harrington & Lambert 2025, Biology Letters).

      We use metrics based on the nature of our task to assess performance and ensure birds have learned before we include them in our analyses, for instance, all birds made < 3 errors on average and completed at least 20 trials. Random chance performance on our task is 3.5 errors (7 possible errors), therefore, birds that make more than 3 errors have not learned the task and therefore would not be included in analyses. It should be noted that this is very rare and typically if birds are excluded its because they do not complete 20 trials.

      See Testing spatial cognition section of the manuscript:

      “In addition, birds were included in analyses if they performed at least 20 trials and made fewer than mean 3 errors across the 4-day testing period.”

      We can certainly understand concerns about individual differences in motivation and neophobia, however, we have also considered this and made efforts to address these issues. That said, we do rely on birds to participate willingly in the spatial task and there may be some bias associated with those that participate vs. those that do not that we cannot account for. Chickadees, as a caching species, are particularly well-suited for our work as they appear to have infinite motivation to obtain and store food items in the wild (and individuals indeed differ in this, hence the differences in the number of trials completed, which we control for in our analyses). We know this not only from our study but work from many researchers, where birds have ad libitum food and continue to cache. Regarding neophobia, birds are introduced to the feeder array in stages, as we explain in the methods, and no humans are present during testing.

      “Each array consisted of eight equidistant RFID-based feeders on a square (122 x 122 cm) aluminum frame suspended ca. 4 m above the ground (Figure 1). Each feeder is equipped with a small, motorized door. Prior to testing, we kept the feeder doors of the arrays open for approximately one month. Next, the feeder doors were closed and programmed to open for any bird with a PIT tag, this allowed birds to habituate to the motorized feeder doors for one week. Finally, we measured the spatial learning and memory abilities of mountain chickadees using a 4-day task. The spatial learning and memory task involved pseudo-randomly assigning each bird to a single feeder in the 8-feeder array that was not its most-visited feeder during the previous two habituation stages.”

      Finally, using the first-20 and 4-day task performance metrics we’ve shown relationships between survival, longevity, genetic variation, and reproductive output, while seeing none of these relationships with the cognitive flexibility task (all with different groups of birds over the years), which we find to be the most compelling evidence that our metric is valid and sensitive.

      (4) Paternity assignment robustness.

      ddRAD-CERVUS with bimodal LODs (Figure S8) is solid, but unassigned EPY (social-genotyped but no sire) implies missing sires (~?% of EPY?). Include all alive males as candidates yearly? Test power simulations for LOD thresholds. 2019 exclusion justified, but multi-year SNP alignment could boost resolution.

      One of the limitations or challenges of our system is that the birds inhabit a highly contiguous forest and our field site is not demarked by boundaries or barriers, meaning we do have undetected males just outside our field site that likely sire offspring and we have no data for. That said, we were able to assign paternity to 630 out of 732 offspring, giving us a success rate of 86%. We have now added this to the first paragraph of the results. For all 732 chicks we knew the social males genotype and could ID presence of EPY, but in that 14% we could not ID the EP male specifically. We did include all alive males as potential sires. This includes males that may not have been nesting in our boxes that particular year, but were detected alive in the previous winter testing season that we were able to genotype.

      We tried a multi-year SNP alignment first, but obtained stronger paternity assignments and differentiation between first and second-tier candidate sires when we ran each year separately. Running the SNP analysis for each year separately resulted in more SNPs to use in each year which increased our ability to confidently assign paternity.

      (5) Mechanistic speculation vs. data.

      Discussion invokes hippocampus genes (GWAS priors) and good genes, but no offspring cognition/survival data. Label as hypotheses; suggest tracking EPY recruitment. No brood size costs for EP sires is key, but monitor long-term nest investment (e.g., feeding rates).

      Indeed, these are informed speculations/hypotheses based on our previous work showing the genetic bases of cognition and high levels of heritability (Branch et al 2022; Semenov 2024, Current Biology). We also know that juvenile survival is predicted by performance on our spatial cognitive task (Sonnenberg et al 2019, Current Biology). However, we do not have offspring survival for these particular EPY (past fledging the nest). We recruit about 5% of the young back into our study site (Whitenack et al. 2025), which makes it difficult to make meaningful inferences at this point. Though, as we continue to collect data in this system we hope to obtain a sample size to address these questions.

      We have collected data on feeding rates at the nest via RFID in some years, however, these measures come with additional caveats as they do not control for the amount/quality of food. Though, preliminary analyses suggest no difference in feeding rates based on cognitive abilities.

      We use the mass of the nestlings at Day 16, which is a common proxy for survival likelihood in Passerines, and we know individuals of higher mass are more likely to survive and be recruited back into the system (Whitenack et al. 2025).

      Reviewer #2 (Public review):

      Summary:

      In this study, the authors ask whether spatial cognition is under sexual selection in mountain chickadees. To do so, the authors examined a large dataset that includes a) spatial cognition data for both males and females (obtained via use of a clever RFID-based feeder system) and b) social and extra-pair paternity nesting data. As predicted, males with higher spatial cognition sired more extra-pair offspring, and extra-pair sires had, on average, higher spatial cognition scores than the males they cuckolded. Interestingly, females with lower spatial cognition scores were more likely to seek extra-pair copulations, potentially to compensate for their own low spatial cognition. Surprisingly, there was no difference in spatial cognition scores between males that sired their own offspring and those that lost paternity at the nest. Also surprising was the fact that there were no differences in patterns of extra-pair paternity and spatial cognition between high- and low-elevation sites. The latter is particularly surprising in that spatial cognition should be under stronger selection at the high elevation site. Overall, this is a fascinating study that demonstrates that spatial cognition - a trait under natural selection as it directly impacts winter foraging and survival behaviour -is also under sexual selection.

      Strengths:

      The authors have a robust dataset (n = 732 offspring sampled over 3 years), high-quality spatial cognition data collected with a procedure that has been well-honed over the years, and couple the data with solid statistical procedures that address many potential covariates and potentially confounding factors. In addition, the authors are careful in the discussion to elaborate on the many potential alternative explanations from the results and questions that are likely to arise in the minds of readers (e.g., how are females assessing male spatial ability?)

      Weaknesses:

      Overall, no major weaknesses were identified in this study. As always, there are editorial issues that I would encourage the authors to consider, including presentation of data/results and clarification on some statistical issues. Overall, however, this is an excellent study that will make an important contribution to our understanding of the evolution of cognition and targets of sexual selection.

      Reviewer #3 (Public review):

      Summary:

      The authors presented evidence that spatial cognition in this population is under sexual selection, with extra-pair males, primarily chosen by the females, having better spatial cognition than males they cuckolded and males with better spatial cognition having more extrapair young.

      Strengths:

      This cognitive ecology study was conducted on a well-known long-term study population of free-ranging mountain chickadees. This strong base, alongside a thorough study design and extensive statistical analyses, enabled the authors to address research questions that few other labs can address, making this a potentially powerful study of broad general interest.

      Weaknesses:

      Throughout the manuscript, there is a focus on the "mean number of location errors per trial over the first 20 trials". Performance changes across trials, so why weren't learning vs peak performance analyzed separately? Similarly, authors also describe results in the context of the entire task, but sometimes in the context of the first 20 trials - why is one prioritised over the other, and why is the emphasis not always consistent? Are the results across the two generally the same? A more thorough explanation addressing all these points is necessary.

      Results are often similar in both metrics, however, the relationship between first 20 and total trials is not 1 to 1 (reported at top of results section and Figure S7a). We suggest that performance in the first 20 trials reflects memory acquisition, while performance across the full task reflects memory retention and recall (Sonnenberg et al. 2019) – both critical components of spatial learning and memory for food-caching birds. Previous work has shown that performance in the first 20 trials and across the entire task is linked to survival (Sonnenberg et al. 2010) and longer lifespan (Welklin et al. 2024) – meaning both metrics reflect fitness.

      We did locate several instances where the metric used is not clear and we have corrected this, thank you.

      To summarize, we reported results for both metrics for analyses related to the main hypotheses, and then reported first-20 trials in the main text for additional tests and provided the entire test data in the Supplemental Materials.

      Lines 429-432: Why was a categorical (i.e., chi-square test) and not a numerical comparison implemented? A numerical statistical test would capture more of the variation (i.e., the number of years separating the social and EPY males).

      We agree and have added a paired t-test to assess whether within each offspring, the social father differed in age from the EP father. This test returned a similar result (χ<sup>2</sup><sub>1</sub>=0.27, P=0.662) and has been added to the results.

      Recommendations for the authors:

      Reviewer #2 (Recommendations for the authors):

      (1) Statistical analysis:

      The authors include elevation as a fixed effect in their models; however, I am curious as to why there were no elevation interaction terms included in the model, as I would predict that selection for higher spatial cognition would be stronger in high-elevation sites.

      We did test for an interaction between elevation and cognition in many of our models and did not find any support for interactions. For example, there was no interaction between elevation and cognition in the number of EPY sired model.

      (2) General comments:

      (a) I would prefer to see extra-pair paternity written out rather than EP, EPY, EPP, etc., as the inclusion of the words takes little extra space in the text and makes for easier reading (personal preference).

      We agree and have spelled out EP, EPY, EPC, and EPP throughout the MS.

      (b) The discussion does an excellent job addressing these questions, but it could be worth noting in the introduction why it is unlikely that this pattern would result simply from selection for dominance (or other factors), given that readers familiar with chickadees would likely immediately think about the role of dominance in extra-pair paternity.

      We have added this point to the introduction, thank you:

      “Previous research in chickadees shows that females often produce extra-pair young with socially dominant males (e.g., 44, 45), however, in our study system we do not see a significant relationship between social dominance and spatial cognitive abilities (46). One potential explanation for this, is that social dominance changes over an individual’s lifetime and with age, while spatial cognition is repeatable and consistent over individual mountain chickadee’s lifetime (25, 27-32).”

      (3) Specific comments:

      (a) Line 27-31: This feels like a weak justification. The study is really about selection on cognition rather than the study of sexual selection per se. I would encourage the authors to revise this justification statement to focus on how sexual selection can help shape cognition.

      We have edited the abstract.

      (b) Line 49: Suggest a stronger start rather than justifying the paper by stating that mate choice has been a focus of evolutionary biology.

      We have edited this statement.

      (c) Line 50: I believe this should be "mate choice" rather than "sexual selection"

      Edited.

      (d) Line 59-62: Confusing wording throughout, especially as "high quality" does not refer to sperm competition, but rather sperm that may have an advantage during sperm competition.

      Suggest reworking the sentence for clarity.

      We changed this to quality instead of competition.

      (e) Line 69: Predictable variation in what?

      We have removed predictable variation.

      (f) Line 87: time;

      Corrected.

      (g) Line 104: Cut "specifically (i.e., caches are not used for provisioning young),".

      Removed.

      (h) Line 106: Presumably referring to assortative mating?

      Added.

      (i) End of the introduction focuses on a couple of primary questions; however, once the reader gets to the methods and results, we find many more tests (e.g., age, brood size, female cognition, etc.). If the authors are going to include these additional tests (which I think they should), then I encourage them to provide the predictions or justifications for those analyses here. In addition, please double-check to ensure the presentation of all tests is consistent throughout the intro/methods/results/discussion.

      We have now added this information to the end of the introduction as alternative explanations, rather than specific predictions. These additional analyses were implemented post-hoc to exclude alternative explanations for the patterns observed, but we have linked them to the statistical analyses as requested by Reviewer 3 and for clarity.

      (j) Suggest including sample size (nests, nestlings, etc. per year) in methods.

      We have provided all sample sizes in the Supplemental Tables and in the text where appropriate.

      (k) Line 255: How many egg dumps? Would be interesting to have a number here for future meta-analyses, reviews, BOTW accounts, etc.

      Unfortunately, we do not have this data available, and the numbers were very low which is why we did not report them in the manuscript. As we continue to work in the system we will be able to provide this information.

      (l) Line 273: See comment above re: elevation as an interaction term in models.

      Addressed above.

      We did test for an interaction between elevation and cognition in many of our models and did not find any support for interactions. For example, there was no interaction between elevation and cognition in the number of EPY sired model.

      (m) Line 282: extra period.

      Removed.

      (n) Line 328: Change "Here" to "Next".

      Edited.

      (o) Line 465: I would suggest bringing this in earlier and considering the flow of how ideas are presented. Seems more logical to first look at whether males lost paternity, then, if so, to whom those males lost paternity.

      We have reorganized the results section to be consistent with the stats methods order.

      (p) Line 553: suggest "likely means" as there may be other attributes of individual quality outside of spatial cognition that are important.

      Yes, we have added this qualifier.

      Reviewer #3 (Recommendations for the authors):

      (1) Section starting at line 268: This section has a long list of statistical analyses. To better help the reader follow the aims of the study and the relationship between them, I recommend numbering the research questions in the introduction and pairing them with the description of each statistical test here in the Statistical Analyses section.

      We agree, thank you for this suggestion.

      We have now added this information to the end of the introduction as alternative explanations, rather than specific predictions. These additional analyses were implemented post-hoc to exclude alternative explanations for the patterns observed, but we have linked them to the statistical analyses and results as requested.

      (2) Lines 295-297: What was the percentage of tested individuals that had brood size data available?

      We only have brood size information for birds that breed in our nestboxes and make it to day 16 post-hatching – this is a much-reduced sample from those that participate in cognitive testing (we test around 150 birds a year between both elevation sites). In addition, throughout breeding stages we lose nests to predation, prior to day 16, when we collect blood samples from the chicks. Birds also participate in cognitive testing and breeding in multiple years, so our sample size for EPP is a result of repeated measures in some instances.

      (3) Lines 473-479: The study is done, so use the past tense throughout the manuscript.

      Thank you, we have edited this for consistency.

    1. eLife Assessment

      This important study examines how temporal and spatial discrepancies between an action and its visual feedback are combined in judgments related to the sense of agency. Using a virtual-reality reaching task and computational modeling, the authors show that both discrepancies influence self-attribution and that a multiplicative model outperforms the prespecified alternatives, a result reproduced in a preregistered replication. The evidence is convincing for the conclusion that temporal and spatial information is integrated. Other models - beyond multiplication - may in principle account equally well for the observed data; sharpening these claims and situating the findings more clearly within Bayesian accounts of cue integration would further strengthen this key contribution.

    2. Reviewer #1 (Public review):

      Summary:

      This study investigates how temporal and spatial discrepancies between an action and its visual feedback are combined when people judge whether an observed movement corresponds to their own action. Using a virtual-reality reaching task and computational modeling, the authors show that both temporal and spatial discrepancies reduce self-attribution and that a Multiplicative model better explains judgments than the prespecified Minimum and Mean alternatives. Importantly, the main model comparison was confirmed in a preregistered replication.

      Strengths:

      The study addresses an important question about how multiple sources of sensorimotor information contribute to the sense of agency. The factorial manipulation of temporal and spatial discrepancies provides a clear experimental approach to this question, and the distinction between an exploratory study and a preregistered replication is a major strength. The behavioral effects were consistent across the two samples, and the replication provides confirmatory support for the advantage of the Multiplicative model among the prespecified candidate models.

      Weaknesses:

      The main limitation concerns the strength of the theoretical conclusions drawn from the model comparison. Although the Multiplicative model outperformed the prespecified Minimum and Mean models, this does not uniquely establish multiplication as the underlying computational operation, because other nonlinear integration rules were not tested. The evidence therefore more directly supports cross-domain integration than a specifically multiplicative computation.

      Similarly, the behavioral results do not by themselves establish that this integration occurs within a domain-general representation of agency. The binary judgment used in the task is also closely related to judging whether the seen and performed movements match, leaving open whether the observed integration reflects agency computation itself or a later decision process. These considerations do not undermine the main empirical findings, but they suggest that the broader computational and representational claims should be stated more cautiously.

    3. Reviewer #2 (Public review):

      Summary:

      Harduf et al. investigated the mechanisms underlying the sense of agency (SoA), the feeling of being in control of our actions. SoA is thought to arise from the comparison of action outcome predictions, emerging from forward models, with the actually observed outcomes. Current literature has not elucidated how cues from different forward models predicting different features of actions are combined to form a final agency judgement. The authors investigate this gap by introducing various combinations of spatial and temporal mismatches during a virtual reality (VR) based reaching task, and recording participants' agency judgements. Then, they fit different models of cue combination to behavioural data and compare them. The authors find compelling evidence that a model in which the effects of spatial and temporal incongruence are multiplied provides the best fit, outperforming models that do not combine spatial and temporal incongruences, and one model that combines them by averaging rather than multiplying. The authors argue that sense of agency emerges from the domain-general integration of different forward models.

      Strengths:

      The experiments are well designed, and model validation is compelling thanks to the comparison with an appropriate set of alternative models and replication of the results in a first exploratory cohort and in a pre-registered validation cohort. The writing is generally very clear, and the presentation of the results is exhaustive yet compact, making the paper very readable. I also commend the authors for empirically addressing whether and how the integration of different cues contribute to the sense of agency, an idea which has been more widely proposed conceptually than empirically investigated.

      Weaknesses:

      (1) The terminology is not always clearly defined, and the authors are not sufficiently rigorous in specifying what gap their study aims to address exactly, and to what extent it succeeds. The authors acknowledge that the idea that cue combination underlies sense of agency was already proposed, yet frame their contribution as showing that "integration" underlies sense of agency. However, it is unclear what the authors exactly mean by "integration". Also, it is not well specified what a domain-general representation is exactly, and how such a notion is not already included in the previously proposed cue combination theory. Overall, the authors seem to imply the main contribution of the paper is to elucidate both whether and how cue integration is performed. However, the most novel contribution of the study appears to concern the "how" rather than the "whether".

      (2) The authors should have discussed their findings in light of Bayesian inference, which has already been proposed as the basis of cue combination for sense of agency. The fact that a multiplication of the different cues fits the data better than an averaging makes it too tempting to think about Bayesian inference to ignore such idea. Indeed, if cues are assumed to provide statistically independent evidence about the probability of self-causation, then they should combine in a multiplicative way rather than by averaging. Even if the experimental design or pre-registration constraints preclude a formal Bayesian analysis, this point should be discussed and related to previous literature.

    4. Author response:

      We thank both reviewers for their constructive and thoughtful evaluations, and the editors for the positive assessment. We are grateful that the reviewers found the question important, the design clear, and the exploratory-replication structure a strength. Below we briefly outline the directions of our planned revision; a full point-by-point response to the public reviews and the recommendations will accompany our resubmission.

      The reviewers raise several points we find valuable and intend to address. First, we will clarify the scope of our central claim. We will distinguish our confirmatory result, that the Multiplicative model best accounts for the data among the preregistered candidates, from stronger statements about the underlying computation, making explicit that the evidence most directly supports cross-domain integration, with multiplication the best-supported rule among those tested. Second, we agree on the importance of situating our findings within Bayesian accounts of cue integration, and will add a discussion of how the multiplicative combination of statistically independent cues relates to probabilistic inference about self-causation and to prior cue-integration work on the sense of agency. Third, we will sharpen definitions of key terms such as ‘integration’ and ‘domain-general’ and the framing of our contribution, clarifying that it concerns how multi-feature prediction errors are combined. Finally, we will address and discuss the distinction between the integration we demonstrate and its interpretation as a domain-general representation of agency.

      We believe these revisions will strengthen the manuscript, and we thank the reviewers again for their helpful guidance.

    1. eLife Assessment

      This valuable study evaluates how different learning curricula in a complex planning task impact performance. The authors provide convincing evidence across eight behavioral studies that practicing increasingly difficult examples of a concept before moving to the next concept benefits immediate performance, despite participants having a preference for the reverse - learning new concepts before practicing. However, there is incomplete evidence for claims about relevance to real-world learning settings, as the curriculum effects did not persist across a delay or support transfer to new problem types. The paper will be of interest to researchers studying learning curricula, problem solving, planning, and education.

    2. Reviewer #1 (Public review):

      This work uses a compelling pairing of controlled task design with rich, naturalistic behavior. It is ambitious, with eight experiments, both correlative and causal demonstrations, delayed assessments, and generalization of the phenomena to a very different domain.

      The stated goal of the paper is to understand how learning different curricula "promote successful learning of complex tasks in human adults," which is a very broad scope. The evidence for generalization to a non-spatial task is compelling, but the structure of the two tasks is isomorphic, and it is still difficult to claim likely generalization to all complex tasks. For example, one could wonder to what extent demands of the task or online setting may have explicitly or implicitly encouraged participants to prefer trials that can be completed more quickly, biasing them towards choosing discovery transitions. Is it possible that they believed that the experiment might end more quickly if they chose trials that were faster to complete?

    3. Reviewer #2 (Public review):

      Summary:

      This paper investigates how curricula should be structured to support learning in complex tasks, focusing on the distinction between concept discovery (learning new components) and concept implementation (learning how to use them in increasingly complex sequences). The main finding is that participants tend to prioritize discovering new concepts, even though learning outcomes are better when they instead practice implementing known concepts in increasingly complex ways.

      Strengths:

      A major strength of the paper is the experimental paradigm. The grid-world task is a novel way of independently manipulating two dimensions of complexity that are often confounded: the number of concepts that need to be learned and the complexity with which those concepts must be deployed. Participants have to learn how the underlying tools operate and use them in different configurations, which provides an interesting setting for studying variability in how individuals structure their own learning. Additionally, the experiments progress from correlational analyses of self-directed learning, to randomized experimental comparisons of curricula, and finally to transfer and adaptive curriculum tests. Overall, this paper addresses an important question about whether the complexity of concept discovery versus implementation is better for structuring learning.

      Weaknesses:

      The main concern is that the paper's conclusions do not seem to fully match the evidence. The authors present the results as identifying a relatively general principle of complex learning, such that we should master a concept's implementation before introducing new concepts. There was no implementation-first advantage in the transfer phase, and when testing was delayed by one week, the curriculum difference was no longer detectable. Although the implementation-first curriculum improves immediate performance within the constraints of these experimental paradigms, it is less clear that it produces more generalizable learning. Additionally, it is not sufficiently clear how the "flow" measure was created, what "flow" means in the context of subjective task experience, or more broadly in everyday learning, and what inferential role the measure plays in the paper.

    4. Reviewer #3 (Public review):

      Summary:

      The authors report findings from a set of experiments that compare learning outcomes from two broad classes of training regimens. The first, concept discovery, emphasizes the identification of task components. The second, concept implementation, emphasizes the procedural deployment of task components. Learners tend to self-schedule the former, but task performance and performance on an immediate test are supported by the latter.

      Strengths:

      The authors address a novel research question with clear application value. The experiments that are reported in the manuscript are effectively designed to answer the research questions of interest. However, as I will expound on below, the authors ultimately misinterpret their findings.

      Weaknesses:

      This manuscript makes no mention of the distinction between learning and performance (e.g., Schmidt & Bjork, 1992; Soderstrom & Bjork, 2015). It is well-known in the human learning literature that valid tests of learning are ones that entail a separation of context from an initial acquisition phase. This separation may be temporal (i.e., a delayed test) or feature-based (i.e., a test of transfer on novel iterations of a task). In any case, contextual differentiation between test and acquisition is critical to provide an accurate assessment of learning. To the authors' credit, they do report results from both a delayed test and tests of transfer. However, they do not appropriately interpret the results of those tests, which demonstrate no impact of training schedule on learning. They instead rely on findings from immediate tests to support their claims that graduated concept implementation supports learning. In truth, concept implementation is neither preferred by learners nor beneficial to learning. These findings are accordingly quite limited in terms of generating insights for effective training conditions.

      Compounding my primary concern above, I will note that the authors report fitting Bayesian multilevel models (MLMs) to their data, yet rely extensively on analyses of participant means (e.g., permutation t-tests) when a proper MLM would suffice, and in fact would be more appropriate. Bayesian MLMs are an incredibly flexible and robust analytic tool (e.g., Fiechter, 2024), and I would have liked to have seen a more comprehensive approach in this manuscript.

    1. eLife Assessment

      The primary aim of this study is to measure the different dimensions of executive function in non-human primates and the classification of animals into distinct cognitive phenotypes. To achieve this, it uses a multi-task touchscreen battery for six rhesus macaques with repeated measurements to examine how performance across inhibitory control, shifting/learning, working memory, and updating covaries within and across individuals. Overall, this is an important finding with relevance for anyone interested in behavioural assessment of cognitive functions in non-human primates or dimensionality of executive functions. The strength of evidence is overall convincing, with some minor methodological weaknesses that should be addressable in future versions.

    2. Reviewer #1 (Public review):

      Summary:

      The authors aim to characterize how executive function (EF) subdomains (shifting/learning, inhibitory control, working memory maintenance, and working memory updating) are organized in NHPs. They train six macaques on four touchscreen tasks per session and derive a battery of behavioral metrics. They use reliability screening, correlation analyses, unsupervised clustering, and latent factor models to argue for separable EF factors and consistent cross-session cognitive phenotypes.

      Strengths:

      (1) Clear behavioral effects within tasks.

      (2) Battery approach with repeated sampling.

      (3) Scalable.

      Weaknesses:

      (1) Fixed task order may be a confound for between-task correlations. Curious about the potential impact of motivational changes, reward satiety, fatigue, etc., especially if later tasks have lower accuracy or higher variance?

      (2) The claim about the common EF factor is limited with only 6 animals.

      (3) The claim about phenotypes and clustering is limited as one cluster is a single animal (subject F).

      (4) Framing of the AS task as such is inappropriate, as it is a manual response task and is not interchangeable with classic oculomotor AS paradigms.

      The authors demonstrate with solid evidence that a multi-task battery can yield repeatable performance metrics across multiple EF-relevant tasks in macaques. The data support the idea that multiple separable dimensions can summarize performance in this cohort and that individuals differ consistently across sessions. However, the claims about the three cognitive phenotypes, four-factor EF architecture, and lack of common EF factor in NHPs are only partially supported given the fixed task order, small sample size, and ambiguity about whether factors reflect stable traits or covariance between sessions. These claims would be strengthened if the authors analyze effects of the fixed task order and time since session start.

    3. Reviewer #2 (Public review):

      Summary:

      The authors set out to characterize how four studied components of executive function (Inhibitory Control, Shifting, Updating, and Working Memory) are organized in rhesus monkeys, using a battery of four touchscreen tasks performed repeatedly across many sessions. They report that individual monkeys fall into distinct "cognitive phenotypes," and that statistical modeling of the task scores reveals four separable latent factors, which they interpret as evidence against a single shared "general" executive function factor in this species. The four latent factors (Shifting/Learning, Exogenous Inhibitory Control, Endogenous Inhibitory Control, and Working Memory Updating) are slightly different from those mentioned in the introduction, which is of interest to refine the definition and assessment of executive functions in non-human primates.

      Strengths:

      (1) The multi-task, repeated-measures design is a substantial undertaking and yields a large, richly characterized behavioral dataset from the same animals across many sessions. The use of 4 different cognitive tasks is uncommon in this field and a real asset to the study.

      (2) The overall approach of testing the reliability of each behavioral measure before including it in further analysis is a sound and appreciated practice, backed up with overall appropriate data analysis strategies.

      (3) The attempt to move beyond single-task designs, which dominate the non-human primate literature, toward a broader profiling of executive sub-functions is conceptually valuable and timely.

      Weaknesses:

      (1) The four factors introduced early in the paper are not the same four factors that emerge from the final statistical analysis, and this shift is not clearly flagged or explained to the reader, which is likely to cause confusion about what the paper is actually demonstrating. In addition, working memory vs. updating are not clearly defined or distinguished. The introduction and discussion sometimes treat "updating" and "working memory" as separate executive functions, but the literature the authors themselves cite (e.g., Friedman & Miyake, 2017) treats updating as a component nested within working memory rather than a fully independent dimension. This conceptual ambiguity carries through the paper and affects how the reported four-factor structure should be interpreted.

      (2) The grouping of very different types of behavioral measures (accuracy-based executive function metrics, reaction-time/processing-speed metrics, and reward/loss motivational-effect metrics) under a single "cognitive metric" label in Figure 2 is potentially misleading, since these are not conceptually equivalent, and the paper later treats them separately in supplementary analyses. This could give readers the impression that all reported metrics are equally informative tests of the four-domain hypothesis, when only a subset actually is.

      (3) The rationale for how motivational & valence effects (gains/losses) were analyzed is not well supported. Effects of loss versus gain may reflect risk attitudes or loss-aversion documented in the primate literature, rather than motivation per se, and this alternative explanation is not discussed.

      (4) The Discussion would benefit from acknowledging other factors that could influence performance and phenotype membership across sessions (such as age, sex, social rank, or stress) and from clarifying whether the identified cognitive phenotypes are expected to be stable traits or could shift with further training. Additionally, some interpretive statements appear in the Results section rather than the Discussion, which is inconsistent with how results and interpretation are normally separated, and could give undue weight to preliminary interpretations.

      (5) Concerns about the reliability of some response-time-based measures. In the Continuous Updating task, reaction times appear to drop unexpectedly at the highest difficulty levels for each animal, which may reflect animals disengaging from the task (e.g., random choice heuristic) rather than a genuine cognitive effect, and could also reflect thinner, unevenly sampled data at these levels rather than a true average. If so, the slope measures derived from these data may need to be recalculated after appropriately filtering the trials. Related to this, individual differences in hand-resting position in front of the screen are acknowledged by the authors as a source of noise in reaction times across tasks, but no correction or normalization for this is applied.

      (6) The Supplementary Methods referenced in the text (e.g., for the variance decomposition and additional factor analyses) do not appear to be included anywhere in the submitted materials, only Supplementary Results, making some analyses difficult to fully evaluate. There is no information about the subject age, sex, species, etc.

      Achievement of aims and impact:

      The authors largely achieve their stated aim of demonstrating that monkeys' executive function performance is reliable and structured in different dimensions rather than guided by a single factor, and the discovery of stable individual "cognitive phenotypes" across sessions is a genuinely interesting and useful contribution. However, the conceptual framework of the study needs to be better clarified (i.e. are working memory and updating truly separable?) and enriched with relevant inputs from the fields of comparative cognition and cognitive ethology in non-human primates and beyond. The conclusions about Response Times (RT) may change after proper or further analysis of these metrics; additional relevant dimensions may then be disentangled by considering the sources of variability in these RT data (i.e., motivation, emotional state). With clarification of these points, this work has the potential to become a useful reference and methodological resource for the field of comparative cognition and non-human primate cognitive neuroscience.

    1. eLife Assessment

      This valuable study uses chemogenetic inactivation techniques in behaving macaque monkeys to ask whether the locus coeruleus (LC), an area that is involved in the control of arousal, is also involved in regulating access to mental resources for a task that requires working memory. The data show that moderate locus coeruleus inhibition impairs foraging efficiency without affecting response times, total rewards accrued, or overall motivation. However, the evidence supporting the more specific claim that the locus coeruleus regulates cognitive and/or working memory function is currently incomplete, as the experimental design is confounded by elapsed time, lacks formal statistical interaction tests, and relies on behavioral measures that cannot cleanly separate effort allocation from working memory capacity or strategy shifts. These limitations may be ameliorated with further analyses.

    2. Reviewer #1 (Public review):

      Summary:

      This study has a very well-posed question and interesting proposed finding. It proposes that because LC activity has been shown to correlate with arousal, perhaps its activity is related to mobilizing resources for cognition and action. They use chemogenetics to inactivate LC neurons while monkeys perform a foraging task with homogeneous reward options and a working memory component. They use two task conditions, one where the grid of reward wells is visible and one where they are opaque, and the monkey has to open them to retrieve the food inside and remember where he has been. The authors claim that inactivation of LC had a selective effect on the opaque well task by increasing the number of revisits to the same wells without affecting total rewards, response times, or overall motivation, which they interpreted as reduced efficiency rather than disengagement. This is a particularly appealing outcome because it implies that LC suppression, which should lower arousal, does not cause disengagement, and rather affects working memory in some fashion. However, there are many issues with this study that make the abstract misleading, in a concerning way. The abstract and introduction are written well, but the data do not support the overall claims, and there are fundamental flaws that aren't easily resolvable.

      Strengths:

      The bigger question is very interesting and has broad appeal. The writing of the abstract and introduction sets the paper up clearly.

      Weaknesses:

      (1) The title and abstract claim effort; the paper repeatedly discusses working memory. It does not appear that any metric of effort was measured or manipulated in this study (no pupillometry, no effort-cost model, no offered choice between effort levels).

      (2) The authors cite their previous paper as strong evidence of chemogenetic manipulation of LC neurons. They use that paper to justify the dose they use of DCZ, the activator of the chemogenetic construct. That study recorded only 5 neurons at that dose, and 3 of them had a reduction in rate. The statistical significance of that result is questionable (given that I think the authors averaged % change across 3 neurons and it was 15%). The authors treat that as a robust result in multiple places, for example:

      "...induced a reliable 15% decrease in the firing of LC neurons..." The only significance test reported in the source paper for that data is t(5.8) = -3.6, p = 0.01, and that test compares low-dose against high-dose neurons. It establishes dose-dependence. It does not establish that 0.1 mg/kg produced a reliable decrease in firing.

      "...the dose of DCZ was chosen based on its known influence on LC activity in monkey Jf, i.e. a 15% decrease in firing lasting over 30 minutes...".

      This is misleading to readers with regard to the effectiveness of chemogenetics in LC and for creating a foundation for this paper.

      (3) The authors seem to undermine their main argument that the opaque task requires working memory at all, and I am not convinced the monkeys were not just operating like a random agent. The random agent model seems to sample uniformly with replacement and an arbitrary termination rule, which is a straw man argument for a model. It does not allow for testing memoryless vs. memory. A monkey executing a systematic scan with short, coherent moves will avoid revisits without using working memory at all. The correct control is an agent matched to the animal's own movement statistics (step-length and turn-coherence distributions) with no memory of visited wells and with memory of visited wells. There's currently no evidence that the monkeys are using working memory in the opaque well task.

      (4) The models used are not described sufficiently for interpretation or replication. The methods are brief, and the models seem pulled out of thin air with no reference to other work in foraging or any mathematical construct. If the authors have used these models before, then they should cite them, but the statements "...performance of a random agent is harder to evaluate intuitively...", "random agents have no reason to stop generating actions...", "...the number of trials was set arbitrarily...", and the qualitative comparison of performance "was intermediate between that of the optimal and average random agent..." raised concern as to whether the models were principled at all. The distance model was strange, although fine, but why not use geometry? It is also not clear why a reach would have this kind of spatial relationship when the monkey eats food between visiting locations and does not go from one box to the other without a reach to his mouth and a reach back to the board in between.

      (5) The figures in this paper need work; axes are not labeled properly, or have labels that are undefined. Legends are not labeled clearly or described properly in the figure legends to interpret the figures. In multiple cases, the box and whisker plots have too little data, and the edges of the boxes collapse on themselves because IQR doesn't make sense for only 6-7 data points.

      (6) The statistics seem questionable; although they used parametric and non-parametric tests, they report results of tests that assume normality for small sample sizes <10. Other choices regarding the trial number to stop counting data (e.g., on the 20th or 23rd) are suspicious and seem arbitrary, not data-driven. Some choices regarding analysis are stated in the text as arbitrary when they could be derived from hypotheses or rigor. The extremely small p-values for the ANOVA result testing performance made me suspect that this was combining all trials across sessions (given there were only 6 or 7 sessions per animal) and there was no repeated measures or nesting to account for repeated sampling within an animal. Sessions within an animal under matched conditions are the natural paired unit. So a paired or mixed-model treatment would be both more powerful and more appropriate.

      (7) Lastly, there is no evidence that chemogenetics actually worked in this experiment in a way that tests their hypothesis. In Figure 7, they show their only effects that passed statistical significance (p=0.03 and p=0.028) for two monkeys, comparing across saline (D) and DCZ (D) sessions, and show a small effect on mean performance. I don't think there were corrections for multiple tests across all their monkeys and performances. But that aside, the monkeys' performances were not different from those of their random agents on mean performance, as shown in Figure 5(A). So if they are performing randomly, what would inactivation be affecting?

      (8) There seems to be an ordering confound of weeks 1 and 2 being saline and weeks 3 and 4 being entirely DCZ, and only the last 2 weeks are randomized, so for 20 of the 28 sessions per animal, treatment is confounded with time.

      (9) There does not appear to be any group x treatment interaction test for any analyses. Claims rest on statements like "significant in monkey 1 but not in monkey 3," etc.

      (10) There are duplicated statistics between pages: "The effect reached significance for monkey Jf (T-tests, Jf: t(10.79) = -2.31, p = 0.042), but only approached significance for monkey Js (t(8.89) = 2.04, p = 0.072)." on page 12, "The number of trials performed after the 23rd reward increased under DCZ for monkey Js (T-test, t(10.79)=-2.31, p=0.042) and Jf only showed a non-significant trend (T-test, t(8.89)=-2.04, p=0.072)." on page 13 for a separate section.

    3. Reviewer #2 (Public review):

      Summary:

      The authors investigate the causal role of the LC in mobilizing cognitive resources. Two rhesus macaques expressing TH-dependent inhibitory (hM4Di) DREADDs in the LC, alongside one non-transduced control, performed a spatial foraging task under visible (transparent) and hidden (opaque) reward conditions. The authors report that systemic DCZ (0.1 mg/kg) administration selectively impaired performance in the memory-guided opaque condition in the DREADD animals, concluding that LC activity is causally required for allocating cognitive effort

      Strengths:

      (1) Achieving cell-type-specific, reversible chemogenetic inhibition in the macaque LC is a substantial technical achievement that successfully bypasses the limitations of systemic noradrenergic pharmacology.

      (2) The experimental setup elegantly isolates memory-guided action selection from global arousal, sedation, or motivational deficits, providing a useful quantitative constraint for LC functional models.

      (3) The introduction and discussion clearly identify the limitations of using indirect pupillometry to assess locus coeruleus activity. The authors thoughtfully frame their targeted manipulation as a critical complement to existing literature without overstating their claims.

      (4) Demonstrating that the DREADD animals navigated the board similarly regardless of reward visibility is a valuable null finding. This specific result provides crucial evidence for addressing the memory-versus-effort debate. Elevating this insight from a subordinate clause to a more prominent position will further maximize the paper's impact.

      Weaknesses:

      (1) The current behavioral metrics-specifically mean inter-choice distance and success rates bounded by a "random agent" baseline-cannot distinguish between a working memory deficit and a fundamental shift in search strategy. Because performance is a many-to-one function of memory quality and search policy, animals could achieve the observed outcomes either by executing a tight local sweep with "leaky" memory for visited wells, or by abandoning tight spatial locality for a dispersed, near-random walk with intact memory. Without a detailed characterization of the animals' actual step-by-step visit sequences and spatial consistency across conditions, the data cannot adjudicate whether LC inhibition degrades an existing habitual policy (a memory/maintenance deficit) or impairs the adoption of a structured policy (an effort-mobilization/task-set deficit). Clarifying this is critical, as the two interpretations engage distinctly different LC-prefrontal mechanisms.

      (2) The analyses of within-session behaviors rely on arbitrary reward-inclusion thresholds (e.g., 20 or 23 rewards) and censoring rules that obscure the temporal dynamics of the task. Because wells are depleted without replacement, the difficulty and working memory burden rise steeply as the board empties. A constant, time-invariant memory deficit could readily produce the observed early vs. late performance pattern, as the detectable cost of forgetting accrues heavily after the 20th reward. Furthermore, early-session failures to maintain an effective policy naturally lead to continued responding after depletion. However, the imposed time caps compress variance exactly where this within-session causal chain would be most evident, leaving the claimed independence of the early and late deficits unsupported.

      (3) The experimental timeline heavily confounds chemogenetic manipulation with elapsed time. Test weeks 1-2 were exclusively saline, and weeks 3-4 were exclusively DCZ, with treatment alternation only occurring in weeks 5-6. Consequently, the critical contrast between opaque saline and DCZ conditions is largely a between-block comparison across a month of testing. Plausible time-varying factors-such as habituation, declining novelty of a daily home-cage task, and accumulating satiety-all directionally align with the reported DCZ impairment, undermining the isolation of the chemogenetic effect.

    1. eLife Assessment

      This study suggests that caspases strengthen the antiviral response by cleaving Drp1, which otherwise restrains mitochondria-mediated type I interferon signaling. The authors report an important finding supported by solid evidence. However, further mechanistic work is needed to firmly establish whether apoptotic caspases are indeed required for Drp1 depletion in virus-infected cells.

    2. Reviewer #1 (Public review):

      Summary:

      Drp1 promotes mitochondrial fission, whereas viral sensing can trigger mitochondrial fusion, promoting MAVS aggregation and the type I interferon response. In this manuscript, Fang et al. describe an unexpected role of caspases induced upon Rift Valley fever virus (RVFV) infection in inactivating Drp1. They show that multiple caspases cleave Drp1 in virus-infected cells and propose that this cleavage boosts mitochondrial elongation and type I interferon signaling. Finally, the authors establish the generalisability of the proposed mechanism in the context of cellular infections with H1N1, SeV, and HSV-1. Caspase-dependent and caspase-independent cell death are important components of host defense against viruses. This work suggests an additional antiviral role for caspases through the mitochondrial type I interferon axis. Although the authors have addressed most of the reviewers' concerns in a rigorously revised manuscript, one aspect of the proposed mechanism requires further validation.

      The data in Figures 6E and 6F remain difficult to reconcile with the study's central thesis. The authors propose that caspase-mediated cleavage of full-length Drp1 enhances the type I interferon response in virus-infected cells. To test this model, they overexpressed the caspase-resistant mutant Drp1-3DE. Yet RVFV infection reduced Drp1-WT and Drp1-3DE levels to a similar extent (Fig. 6E), while progeny virus yield was higher in cells overexpressing Drp1-3DE (Fig. 6F). This observation contradicts the notion that the abundance of Drp1 in virus-infected cells is primarily regulated by Caspases and also weakens the potential link between Caspases and viral propagation through Drp1. The authors' rebuttal that the transcription-inhibitory function of NSs may have contributed to the reduction of both Drp1-WT and Drp1-3DE is neither persuasive nor helpful in resolving the mechanistic discrepancy. Because this is a very significant point, clarity on this matter will be critical.

    3. Reviewer #2 (Public review):

      In the present study, the authors report the role of virus-induced apoptosis in positively regulating innate immune response. Upon infection, host cell apoptosis is triggered as a defense mechanism against virus replication. Culmination of infected-cell death impairs replicative potential for viruses hence attenuating virus propagation. Reports exist denoting inhibitory effect of apoptosis upon innate immune signaling. Contrary to that, findings of this manuscript underscore possible role of apoptosis in enhancing innate immune signaling and effector response. Infection-induced activation of caspases (3, 6, 7 and 8) has been demonstrated to cleave DRP1 protein. DRP1, acting as positive regulator for mitochondrial fission, upon degradation leads to altered mitochondrial morphology (elongation).

      Mitochondria being a hub for innate immune signaling (via operation of RLR-MAVS-downstream effector molecule-axis), upon elongation as a result of DRP1 depletion results in greater innate immune signal flux and interferon induction. Increased interferon induction thus acts to inhibit virus propagation, as demonstrated by the authors using cell-culture models.

      Strengths:

      (1) The findings presented by the authors have been validated by employing elaborate biochemical experimental approaches. The study entails extensive biochemical characterization of DRP1 residues targeted by activated caspases, in vitro assays validating caspase-mediated DRP1 cleavage & caspase-DRP1 interaction.

      (2) This study possesses broad implications since authors demonstrate role of caspase-mediated DRP1 cleavage in promoting innate immunity, in context of infection by diverse viruses (both RNA and DNA viruses).

      Weakness:

      Although the authors undertook a thorough experimental approach attempting to validate their findings, all the experiments were performed using either cell-culture models for infection or in vitro biochemical assays (cleavage and protein-protein interaction). Additional experimentation using animal models (in vivo) will further help strengthen biological significance of their findings under more physiological settings.

      Comments on revised version.

      I am satisfied with the revised version of the manuscript.

    4. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Drp1 supports mitochondrial fission (doi: 10.1038/s41586-019-1296-y). Viral sensing triggers mitochondrial fusion, leading to MAVS aggregation and improved type-1 IFN response. It was suggested that impairment of Drp1 upon phosphorylation by Tbk1 enhances mitochondrial fusion in virus-infected cells (doi.org/10.1016/j.molcel.2020.10.018). In this manuscript, Fang et al. describe an unexpected role of caspases activated upon Rift Valley fever virus (RVFV) infection in inactivating Drp1. They show that Drp1 is targeted by multiple caspases, including caspase-3, -6, -7 and -8. Indeed, cleavage of Drp1 leads to mitochondrial elongation, boosting the type-1 IFN response of infected cells. Finally, the authors establish the general disability of the proposed mechanism in the context of cellular infections with H1N1, SeV, and HSV-1. Caspase-dependent and independent cell death processes provide important host defence mechanisms against obligatorily intracellular viral pathogens. This work suggests that caspases reinforce antiviral response involving also the mitochondria-type 1 IFN axis. As such, the manuscript is well written, and the proposal pertaining to caspase-mediated targeting of Drp1 may have implications beyond host-virus interaction studies. However, several loose ends remain, and these concerns need to be addressed to substantiate the mechanistic model.

      Reviewer #2 (Public Review):

      In the present study, authors report the role of virus-induced apoptosis in positively regulating the innate immune response. Upon infection, host cell apoptosis is triggered as a defence mechanism against virus replication. Culmination of infected-cell death impairs replicative potential for viruses, hence attenuating virus propagation. Reports exist denoting the inhibitory effect of apoptosis upon innate immune signalling. Contrary to that, the findings of this manuscript underscore the possible role of apoptosis in enhancing innate immune signalling and effector response. Infection-induced activation of caspases (3, 6, 7, and 8) has been demonstrated to cleave DRP1 protein. DRP1, a positive regulator for mitochondrial fission, degradation leads to altered mitochondrial morphology (elongation).

      Mitochondria, being a hub for innate immune signalling (via operation of RLR-MAVS-downstream effector molecule-axis), upon elongation as a result of DRP1 depletion, results in greater innate immune signal flux and interferon induction. Increased interferon induction thus acts to inhibit virus propagation, as demonstrated by the authors using cell-culture models.

      Strengths:

      (1) The findings presented by the authors have been validated by employing elaborate biochemical experimental approaches. The study entails extensive biochemical characterization of DRP1 residues targeted by activated caspases, in vitro assays validating caspase-mediated DRP1 cleavage & caspase-DRP1 interaction.

      (2) This study possesses broad implications since the authors demonstrate the role of caspase-mediated DRP1 cleavage in promoting innate immunity in the context of infection by diverse viruses (both RNA and DNA viruses).

      Weaknesses:

      Although the authors undertook a thorough experimental approach attempting to validate their findings, all the experiments were performed using either cell-culture models for infection or in vitro biochemical assays (cleavage and protein-protein interaction). Additional experimentation using animal models (in vivo) will further help strengthen the biological significance of their findings under more physiological settings.

      Reviewer #3 (Public Review):

      Summary:

      The authors demonstrated that the NSs protein of RVFV triggers the activation of apoptotic caspases, which cleave the mitochondrial fission factor DRP1 resulting in mitochondrial elongation.

      Strengths:

      The manuscript provides an insightful investigation into a novel mechanism through which apoptotic caspases promote anti-viral immunity by regulating mitochondrial morphodynamics.

      We are very grateful for this constructive review. Our specific responses are enclosed below.

      Recommendations for the authors:

      Reviewing Editor Comments:

      It is advised that the deficiencies and weaknesses pointed out by all three reviewers are meticulously addressed. This will be important to mechanistically substantiate the proposed model. While the absence of animal data represents a deficiency, the authors may graphically/textually articulate future courses of in vivo experiments that may shed further light on the physiological significance of the proposed mechanism.

      Reviewer #1 (Recommendations for the Authors):

      (1) Figure 1: While Figure 1 is convincing, could it be possible to add some quantification for mitochondrial morphodynamics? For example, the average number, size, perimeter, interconnectivity, and elongation of mitochondria from uninfected and infected cells may be compared in a violin plot. This approach should then be followed throughout the manuscript.

      We appreciate the reviewer’s suggestion. We agree that quantitative analysis of mitochondrial morphology would be valuable to the study. However, it seems that a widely accepted quantification standard for the mitochondrial elongation phenotype is currently not available. We also tried to use the MiNA (Mitochondrial Network Analysis) plugin (ImageJ; 2017 Acta Histochem, doi:10.1016/j.acthis.2017.03.001) to analyze mitochondrial morphology. Unfortunately, this plugin could not be properly executed due to unknown reasons. For this reason, we propose not to include the quantitative data in the main manuscript, also because the immunofluorescence analysis showed a rather distinctive phenotype. If the reviewer can suggest a method for this kind of quantification, we could try with the suggested method.

      (2) Figure 2: NSs of RVFV interact with the general transcription factor TF-IIH and primarily reside in the nucleus. Then, what could be the mode of caspase activation by NSs? Is the interaction between NSs and TF-IIH important for caspase activation? In general, the link between NSs and caspases may be discussed more elaborately.

      Regarding the mode of caspase activation, our recent publication (together with other’s previous work) showed that the NSs-mediated TFIIH degradation leads to transcriptional inhibition resulting in rapid downregulation of the short-lived pro-survival protein MCL-1, contributing to BAK activation (2024 Cell, doi: 10.1016/j.cell.2024.09.008; 2024 PLoS Pathog, DOI: 10.1371/journal.ppat.1012387). Activation of BAK may trigger mitochondrial apoptosis (2022 Nat Commun. doi: 10.1038/s41467-021-27851-y).

      (3) Figure 3: Some data quantification will be important for immunoblot analyses. This is relevant for other figure panels as well.

      We have now included semi-quantitative analyses for the key immunoblot data in Figure 3.

      (4) Figure 3: Previous studies implicated MITOL-mediated pathway in Drp1 degradation (doi: 10.1242/jcs.257808). Importantly, the ubiquitin-proteasome system was shown to degrade PKR in RVFV-infected cells (doi.org/10.1371/journal.ppat.1005437). It could be important to determine the PKR level in immunoblot analyses performed in Figure 3A to ensure that MG-132-mediated inhibition was not inadequate in those experiments.

      We have now included detection of PKR in the revised Figure 3A and updated the corresponding text in the manuscript. The results showed that MG132 treatment effectively rescued the RVFV-induced degradation of PKR. In contrast, MG132 treatment did not inhibit RVFV-induced DRP1 downregulation.

      These results indicate that while proteasomal degradation of DRP1 may occur in other contexts, it is not the primary mechanism for DRP1 downregulation in the context of RVFV infection.

      (5) Figure S2F and Figure S2G assure the necessity and sufficiency of NSs in caspase activation and should be placed in the main text. These figures MUST also include an additional panel comparing the level of full-length and cleaved Drp1.

      We have reorganized the figures in the revised manuscript accordingly. The changes are as following:

      (1) The original Figure 3G (showing caspase activation during RVFV infection) has been moved to the Supplementary Information as new Figure S2F to streamline the main figures.

      (2) The data showing that NSs expression alone is sufficient to trigger caspase activation and PARP-1 cleavage remains as Figure S2G, with DRP1 downregulation shown separately in Figure 2E from the same samples. This arrangement was maintained for narrative flow in the main text, but we have updated the text to explicitly guide the reader between these figures.

      (3) We have put the original Figure S2F to the main text as new Figure 3G. This figure now includes the DRP1 cleavage data from the same experiment. And we compared the level of full-length and cleaved Drp1.

      (6) Figure 4: Figure 4A needs a positive control to show that caspase 1, -4, and -9 were active in the in vitro assay condition.

      As suggested, the corresponding positive controls of caspase-1, -4, and -9 have now been included in the revised Supplementary Figure S3.

      The enzymatic activities were confirmed as follows:

      Caspase-1 and Caspase-4: We tested cGAS as a reported substrate (2017 Immunity, doi:10.1016/j.immuni.2017.02.011). While the cleavage fragments for cGAS were not observed potentially due to the epitope affinity of the antibody, a clear reduction in full-length cGAS level was observed upon incubation with both caspase-1 and caspase-4 (Figure S3A), indicating substrate degradation by these caspases.

      Caspase-9: Its activity was demonstrated by its ability to process a catalytically inactive mutant of procaspase-3 (Figure S3C).

      (7) There are some anomalies related to the size of the cleaved Drp1, and this needs serious attention. Figure 3E suggests the size of the cleaved Drp1 produced in RVFV-infected cells to be ~ 55 kDa. However, Figure 4A and Figure 4D indicate that the size of the cleavage products is around 15 kDa [Drp1(1)] and 35 kDa [Drp1(2)]. This must be clarified. Did the Drp1(D556E) mutant still produce the 55 kDa band upon virus infection?

      Below, we provide a point-by-point response.

      (1) Clarification on the apparent size discrepancy of cleaved DRP1 fragments:

      The apparent size difference arises from the use of different antibodies targeting distinct regions of the DRP1 protein.

      In Figure 3E, we detected endogenous DRP1 using an anti-DRP1 monoclonal antibody (CST, #5391) by Western blot. Under these conditions, this monoclonal antibody recognize a single linear epitope. The detection of the ~55 kDa fragment provides evidence that the epitope for this monoclonal antibody is located N-terminal to the D500/D503 cleavage site. Therefore, the ~55 kDa band represents the large N-terminal fragment generated after caspase cleavage.

      In contrast, Figures 4A and 4D analyze exogenously expressed, C-terminally strep-tagged DRP1 (DRP1-strep) using an anti-strep antibody. This setup visualizes the C-terminal cleavage fragments (~15 kDa or ~35 kDa) that contain the strep tag.

      These experiments are designed to detect different portions of the cleaved DRP1 protein. We have also revised the respective figure legends to state the protein source and detection method to prevent confusion.

      Specific Cleavage Pattern of the DRP1(D556E) Mutant:

      The full-length DRP1-strep protein is 83.2 kDa. Cleavage at D556 produces an estimated ~61.9 kDa N-terminal fragment and an estimated ~21.3 kDa C-terminal fragment (containing the strep-tag). Cleavage at D503 produces an estimated ~56.3 kDa N-terminal fragment and an estimated ~26.9 kDa C-terminal fragment (containing the strep-tag). Therefore, the DRP1(D556E) mutant would not produce the N-terminal ~61.9 kDa fragment (cleaved DRP1 (1), Author response image 1) and the C-terminal ~21.3 kDa fragment. However, since the D503 site remains intact, this mutant can still be cleaved at D503, yielding the ~56.3 kDa N-terminal fragment (cleaved DRP1 (2), shown in Author response image 1) and the ~26.9 kDa C-terminal fragment. When detected with an anti-DRP1 antibody, the ~56.3 kDa N-terminal fragment from D503 cleavage is observed. This fragment migrates at approximately 55 kDa on immunoblots. The result (see Author response image 1) are consistent with this interpretation and reflect the situation during virus infection.

      Author response image 1.

      (8) Figure 5: It will be meaningful to add a panel on mitochondrial morphodynamics upon Drp1 knockdown in this Figure. This may provide a correlative argument linking mitochondria to type 1 IFN response through Drp1.

      According to this suggestion, we performed this experiment and included a new panel showing mitochondrial morphology in DRP1 knockout cells. The new Figure 5D demonstrates that DRP1 knockout induces significant mitochondrial elongation compared to control cells.

      (9) Figure 6 and Figure 7: One may consider combining these in one consolidated panel.

      We have now combined the original Figure 6 and Figure 7 into a single consolidated panel (new Figure 6 in the revised manuscript).

      (10) Figure 7: Figure 7B needs particular attention. It seems that RVFV infection causes a similar downmodulation of both Drp1-wt and Drp1-3DE. However, Drp1-3DE did not produce the 15 kDa cleavage product and did not support enhanced type 1 IFN response as much as its WT counterpart did. Could the proteasomal pathway have a role in degrading caspase-resistant Drp1? Could the cleavage product have a role in enhancing anti-viral pathways? This must be meticulously addressed involving additional experimental designs, keeping in mind the results obtained using Drp1 knockdown cells (Figure 5).

      We thank the reviewer for this comment. (1) Regarding the similar downmodulation of DRP1-WT and DRP1-3DE, the RVFV NSs has a potent function to inhibit host transcription. The exogenous DRP1-WT and DRP1-3DE are expressed from a CMV promoter via lentiviral transduction. This high-level exogenous expression system is sensitive to the transcriptional inhibition caused by RVFV-NSs, potentially explaining the observed similar reduction in protein levels. (2) Regarding the role of proteasomal pathway in degrading caspase-resistant Drp1, our data indicate that the proteasomal pathway is not the primary mechanism for the downregulation of the caspase-resistant DRP1-3DE. This is supported by the observation that the downregulation of endogenous DRP1 was largely insensitive to MG132 treatment (Figure 3A). (3) Whether the cleavage product plays a role in enhancing anti-viral pathways is an interesting question, which we propose to address in future study considering the scope of the current study.

      (11) Figure 7: A panel may be added to compare the effect of overexpression of WT and mutant Drp1 on mitochondrial morphodynamics.

      According to this suggestion, we have performed the experiment, and the data are included as a new panel in the revised Figure 6H. The results indicate that upon RVFV infection, control cells (PCDH) exhibited significant mitochondrial elongation. In contrast, cells overexpressing either DRP1-WT or DRP1-3DE did not show significant mitochondrial elongation morphology.

      (12) Figure 8: Considering the known role of Drp1 in MAVS aggregation (doi.org/10.1016/j.molcel.2020.10.018), this figure in its present form could appear less informative. One may argue that a comparison of WT and mutant Drp1 for controlling virus-induced MAVS aggregation could be more meaningful.

      We thank the reviewer for this comment. The major finding of our work is revealing a novel, caspase-dependent mechanism of DRP1 down-regulation. Figure 8 (revised Figure 7) showed that depletion of Drp1 promoted MAVS aggregation resulting in a strong antiviral effect. Regarding the suggestion to compare DRP1-WT and DRP1-3DE for MAVS aggregation during virus infection, we have focused our analysis on the DRP1 knockout system. This approach mimics the natural outcome of infection, where virus-induced caspase activation leads to the depletion of endogenous DRP1. Overexpression of WT and mutant Drp1 could hamper virus-induced depletion of Drp1 due to it’s high abundance, and may therefore affect the MAVS aggregation analysis.

      (13) Figure 9: This figure establishes that Drp1 is cleaved by caspases in response to cellular infection by multiple viruses. However, the significance of caspase-mediated Drp1 cleavage per se in the progeny virus titre remains unclear.

      We agree with this comment. The purpose of the original Figure 9 (revised Figure 8) is to show that the caspase-dependent Drp1 cleavage may occur in response to different virus infection. Whether and how this may affect progeny virus production, would be an interesting topic for future investigation. According to this comment, we reworded our description in the corresponding Results section.

      (14) Would caspase inhibition impact mitochondrial morphodynamics and progeny virus titres? This question needs to be experimentally addressed.

      Our data in Figures 3C, 3D, and 3F showed that the pan-caspase inhibitor Z-VAD-FMK effectively blocks virus-induced DRP1 cleavage in a dose-dependent manner. However, this upstream inhibition did not result in a significantly variation of nucleoprotein (NP) expression under RVFV infection. It is possible that the broad-spectrum inhibition of caspases by Z-VAD-FMK may simultaneously affect multiple aspects of the host cell and affect virus-host interactions.

      For this reason, we employed the caspase-resistant DRP1-3DE mutant (revised Figure 6), which allowed us to specifically analyze the functional impact of Drp1 cleavage without introducing the effects of global caspase inhibition and caspase-dependent processes. The result that DRP1-3DE enhances viral replication provides evidence for the role of DRP1 cleavage in restricting virus production.

      (15) One weakness is a lack of in vivo data despite the availability of Drp1-floxed mice. What are the results that one may anticipate based on the current study? This should be indicated in the Discussion section.

      We thank the reviewer for this suggestion. According to this suggestion, we have now added discussion of the anticipated outcomes of in vivo studies.

      Reviewer #2 (Recommendations for the Authors):

      (1) Based on the DRP1-residues that caspase-3 targets for cleavage (500/503 and 556), 3 fragments having molecular weights of 55, 5.5 and 22 kDa, approximately, should be generated. In Figure 3E, the authors show the presence of DRP1 cleavage product having the size of approx. 50 Kda. Since the antibody (anti-DRP1) used by the authors recognizes the C-terminal region of DRP1, immunoblot for DRP1 under RVFV infection context should also demonstrate the presence of ~20 Kda DRP1 fragment. The authors should provide immunoblots showing the same.

      We agree with the reviewer that, based on the identified cleavage sites at D500/D503 and D556, a C-terminal fragment of approximately 20 kDa should be detectable.

      In our experiments using whole-cell lysates, we consistently observed the ~55 kDa cleaved DRP1 fragment but were unable to detect the smaller ~20 kDa fragment with the anti-DRP1 monoclonal antibody (CST, #5391). According to the manufacturer’s information, this antibody was raised against the carboxy-terminal region of human DRP1; however, the exact epitope has not been disclosed (we tried to contact the company, but did not get an answer). Since this is a monoclonal antibody, we infer that this single epitope likely resides upstream of the caspase-3 cleavage sites (D500/D503 and D556). Consequently, the C-terminal cleavage fragment may lack this epitope and is not recognized by the antibody.

      To verify the generation of this C-terminal fragment, we performed in vitro cleavage assays using C-terminally strep-tagged DRP1 (DRP1-strep), where an anti-strep antibody clearly detects the ~20 kDa band (as shown in Figures 4A and 4D–4F). We have included this information in the manuscript for clarity.

      (2) In Figure 4A, although the band for active caspase3 (from in vitro cleavage assay) is missing, DRP1 shows cleavage. Similarly, in Figure 4E, the band for active caspase3 is also missing. The authors should provide explanations for these data.

      The reason is that under our standardized in vitro cleavage assay conditions, the anti-His antibody did not efficiently detect these particular commercial recombinant proteins, likely due to variations in epitope accessibility, protein conformation, or the detection sensitivity of the Western blot. The enzymatic activity of caspases used in our assays was verified using established positive control substrates. For caspase-3, activity was confirmed by cleavage of the well-characterized substrate PARP1 See Figure S3B.

      (3) In Figure 4F, WT-DRP1 full length-band exhibits reduction in intensity upon treatment with caspase3, which is expected given the ability of caspase3 to cleave DRP1. However, although mutating 'D500/556E & D500/503/556E' abolishes DRP1 cleavage by caspase3, total DRP1 band intensity exhibits marked reduction in both cases. The authors should provide an explanation for this anomaly.

      The recombinant protein used in this assay is C-terminally strep-tagged DRP1 (DRP1-strep), detected with an anti-strep antibody. The mutation of sites D500/503/556E prevented cleavage at these positions. However, the observed decrease in full-length protein indicates that caspase-3 cleavage may occur at other, alternative sites within DRP1, such as D221 (Figure 4C). The detection of cleavage fragment is dependent on the anti-strep antibody recognizing the C-terminal tag. Cleavage at alternative sites located between the mutated sites and the C-terminus is likely to produce shorter C-terminal fragments that may be difficult to be detected. Therefore, it is possible that although caspase-3 cleavage proceeds at these alternative sites in the mutant proteins, the fragments remain undetectable with our detection method.

      (4) In Figures 7B and 7D, infection by RVFV of cells overexpressing DRP1-3DE (caspase-mediated cleavage-resistant) also leads to a reduction in full-length DRP1, although no cleavage product was seen. Does this indicate a possible role for other DRP1 residues being targeted by caspases upon virus infection? Treatment of DRP1-3DE-overexpressing RVFV-infected cells with a pan-caspase inhibitor will further provide insight into whether other DRP1 sites can also be targeted by caspase activity. Moreover, the authors should treat RVFV-infected cells with pan-caspase inhibitor and verify whether WT-DRP1 cleavage is restored concomitant with the abrogation of interferon induction and signaling.

      We thank the reviewer for this comment. (1) For the reduction of full-length DRP1-3DE in RVFV-infected cells, similar with the response to the above comment, it is possible that the transcription inhibition effect of RVFV lead to reduction of Drp1-3DE expression level, an effect which may be more pronounced in the exogenous overexpression system. Additionally, the existence of other potential caspase cleavage sites, such as D221 (Figure 4C), may also in part account for the observed reduction. (2) Regarding the suggestion to use pan-caspase inhibition to analyze interferon signaling, we feel that blocking multiple caspase activities with the pan-caspase inhibitor Z-VAD-FMK will likely affect the global caspase activity in the cell affecting the scope beyond the interferon signaling pathways and is likely to complicate the interpretation of the results. Therefore, we employed the DRP1-3DE mutant for the analysis (revised Figure 6). This way we would not introduce the unspecific effects of global inhibition of multiple caspase-dependent processes. The result that DRP1-3DE enhances viral replication (revised Figures 6E-6G) provides evidence for the role of DRP1 cleavage in antiviral immunity.

      (5) MAVS aggregation shown by SDD-AGE in Figures 8B, 8C & 8D does not display a typical appearance characterizing MAVS aggregation. The pattern shown in the above-mentioned immunoblots is very atypical for aggregates and indicates an increased MAVS abundance in RVFV-infected (8B)/DPR1-KO (8C, 8D) lanes. The aggregation of MAVS when demonstrated in SDD-PAGE gels exhibits a completely different pattern. The authors should include positions for molecular weight markers indicating their corresponding molecular sizes in the immunoblot. That will help in differentiating MAVS monomer from the aggregates and will help assess the extent of MAVS aggregation upon infection/DPR1-KO.

      We thank the reviewer for this comment.

      In the meantime, we checked literatures detecting the pattern of MAVS aggregation using the same technique (e.g., 2011 Cell, DOI: 10.1016/j.cell.2011.06.041; 2017 Nature Communications, DOI: 10.1038/ncomms15138), where, if we understood correctly, MAVS aggregation showed a similar high-molecular-weight smear. It is common in SDD-AGE not to use protein markers, as this method separates by molecular size under semi-denaturing conditions and is designed to resolve high-molecular-weight aggregates rather than provide precise molecular weights.

      (6) In Figure 9D, the authors should also immunostain cells with virus-specific antibodies. This will help delineate that only virus-infected, but not uninfected cells exhibit elongation of mitochondria (as indicated by the appearance of TOM20 staining). In addition to that, the authors should provide an immunoblot for SeV antigen in Figure 9F. Instead of upregulating the H1N1 antigen, caspase inhibition by zVAD-FMK displayed a reduction in viral protein concomitant with DRP1 cleavage inhibition. Whereas, HSV-1 antigen abundance was seen to be enhanced upon the inhibition of apoptosis, which is consistent with the activity of axis entailing apoptosis-DRP1 cleavage-mitochondrial elongation-enhanced innate immunity- reduced virus propagation. The authors should explain this discrepancy.

      Regarding Figure 9D (revised Figure 8D) and virus-specific staining, we now provided the viral antigen (NP) staining for RVFV and H1N1 (See Author response image 2). However, highly specific antibodies for SeV and HSV-1 immunofluorescence (IFA) are not available for us at the moment. We therefore propose to keep the original layout of the figure.

      Author response image 2.

      Regarding the immunoblot for SeV antigen in Figure 9F, we now provided the immunoblot for SeV antigen. See Figures 8F.

      Regarding the H1N1-NP level upon Z-VAD-FMK treatment in Figure 9E, the reduction in NP signal with Z-VAD-FMK can be explained by the fact that the H1N1-NP protein itself is a substrate for caspase cleavage, as previously reported (DOI: 10.1007/s00705-007-0003-8). A zoom-in view of the blot in Figure 9E reveals a smaller migrating band corresponding to the cleaved NP. Z-VAD-FMK treatment inhibited the generation of this cleavage band, concurrently with the inhibition of DRP1 cleavage. When the ratio of internal control tubulin was considered, the NP level in the Z-VAD-FMK treatment group would not indicate a reduction of signal.

      Figures 8E in the revised manuscript and Author response image 3.

      Author response image 3.

      (7) Previous report (Wu et al, 2022, Nat cell death & disease, DOI: 10.1038/s41419-022-05156-2) demonstrates the inhibitory role of apoptosis in interferon-induction upon HSV/SeV in macrophage cells. However, in this study, the authors suggest apoptosis positively regulates interferon response (upon SeV/HSV-1 infection of macrophage cells). The authors should provide an explanation for possible reasons accounting for this difference in the discussions section.

      We propose that the functional consequence of caspases activation, which process different substrates, will likely pose two-sided effects on immune signaling as a two-sided “sword”. While the established caspase-dependent cleavage of molecules such as cGAS, MAVS, or NF-κB may pose inhibitory effect, the caspase-dependent cleavage of other substrates (such as Drp1 and yet-to-be identified other substrates) may pose the opposite effect. The overall effect would need to be determined in a context-dependent manner. We have included this notion in the revised Discussion section.

      Reviewer #3 (Recommendations for the Authors):

      (1) Please add the full name in the Abstract.

      We have now added the full names for all relevant abbreviations upon their first appearance in both the Abstract and the Introduction sections.

      (2) There is no scale bar in Figure 1A, 2A, 2B, 9D upper panel, Figure 8A.

      We have now added scale bars to the revised figures: upper panels of Figure 1A, 2A, 2B, and 8D (original 9D), and Figure 7A (original 8A), as suggested.

      (3) Can the author explain more in the text why DRP1, but not other mitochondria proteins?

      Our initial investigation simultaneously examined the key regulators of both mitochondrial fusion (MFN1, MFN2, OPA1) and fission (DRP1) using the same set of cell samples. This comparison showed that DRP1 was specifically down-regulated upon RVFV infection (Figure 2C).

      (4) Can the author explain more in the text why further study H1N1, SeV, HSV-1, but not other viruses?

      H1N1, SeV and HSV-1 are model RNA or DNA viruses that are commonly used and were selected as model viruses in our system. We propose that other viruses could also be investigated in further studies.

    1. eLife Assessment

      This manuscript addresses an important and conceptually ambitious question by using a synthetic biology strategy to perturb ATP homeostasis in yeast and examine its relationship with replicative lifespan. While the lifespan phenotype is robust and the experimental approach is generally solid, the current evidence is limited to establish intracellular ATP abundance as the causal determinant of lifespan, particularly given the apparent uncoupling from NTT1-dependent ATP import and single-cell ATP levels, as well as the possibility of extracellular ATP-induced stress responses. Further clarification of the mechanistic interpretation, including more cautious treatment of Snf1/AMPK signaling and NTT1 transport directionality, will be needed before the biological significance of the findings can be fully assessed.

    2. Reviewer #1 (Public review):

      I thank the authors for a thorough revision. The correction to the QUEEN reporter sign convention resolves what was the central issue in my original review: with the reciprocal (1/488 nm) now plotted in Figure 5, the single-cell reporter data and the LC-MS/MS measurements in Figure 2 are internally consistent, and I appreciate the added metabolomics (Figure S2B) and transcriptomics (Figure S2A, S1-S3) that support reduced de novo purine biosynthesis as the source of the lower baseline ATP in NTT1 cells, as well as the quantified Hog1 phosphorylation data (Figure 4C, Figure S8). However, several points from my original review are not yet fully resolved, and I would like the authors to address them before I can recommend acceptance.

      (1) A residual, uncorrected statement about QUEEN signal directionality. The Results section (subsection "Altered intracellular ATP level modulates lifespan") still states: "...only 488 nm excitation was used in these experiments, and the resulting QUEEN fluorescence signal is inversely correlated with intracellular ATP levels (Fig. 5A-C)." This directly contradicts the corrected Methods section ("...plotted the inverse of fluorescence measurements (1/488nm), as an indicator of ATP abundance, in Fig. 5, A-C") and the Figure 5 legend and color scale, both of which now correctly state that the plotted quantity increases with ATP abundance. Given that this exact ambiguity was the source of the original contradiction the reviewers raised, I ask the authors to correct this sentence and re-check the rest of the text for any other place where the old (pre-correction) sign convention may still be described.

      (2) AMPK/Snf1 validation remains indirect. I had asked for AMPK-pathway controls alongside the Hog1 quantification. The revision addresses this only through transcriptomic inference - downregulation of Snf1 target genes (MIG1, HXK1, HXK2, HXT3; Figure S7) is used to argue for low Snf1 activity under high ATP. This is a reasonable correlative observation but is not a direct validation of Snf1/AMPK activity (e.g., Snf1 Thr210 phosphorylation status or a kinase activity assay), which is what I intended by "AMPK controls." Please either add a direct biochemical readout of Snf1 activation status analogous to the Hog1 blots or explicitly temper the claims about Snf1-mediated metabolic rewiring to reflect that they rest on transcriptomic inference alone.

      (3) Bidirectional-transport hypothesis addressed indirectly, not experimentally. The new metabolomics (ATP/ADP/AMP all reduced in untreated NTT1 cells) and the coordinated downregulation of the purine biosynthesis pathway are a coherent case for "reduced synthesis" as the parsimonious explanation, and I appreciate this analysis. However, this remains a correlative/transcriptomic argument rather than the direct flux experiment I suggested was feasible (e.g., an isotope-labeled ATP efflux/uptake assay to directly test whether NTT1 exports as well as imports ATP). A contraction of the adenylate pool via reduced synthesis and a bidirectional transporter are not mutually exclusive, and the current data cannot fully distinguish them. I would ask the authors to either perform such a direct test or explicitly acknowledge this as a limitation and an alternative that current data do not rule out.

      In summary, the revision has substantially improved the manuscript and resolved the central ATP-directionality contradiction, but points 1-3 above should be addressed, particularly point 1, since it re-introduces the same ambiguity, the revision was meant to eliminate, before I can consider my concerns fully resolved.

    3. Reviewer #2 (Public review):

      Summary:

      This work presents interesting findings where addition of exogenous ATP extends replicative lifespan of yeast cells in a way that seems uncorrelated with actual increased intracellular ATP levels or mitochondria. To be clear, addition of ATP to yeast growth media increases the number of cell divisions per cell in yeast. Expression of the NTT1 ATP transporter gene increases intracellular ATP levels according to LCMS analysis, but the effect on replicative lifespan works without the NTT1 gene and without an intracellular increase in ATP (possibly with a decrease in intracellular ATP), so the effect appears to be independent of the effect on intracellular ATP levels or mitochondria, as mitochondria-less R0 yeast cells also have increased numbers of cell division when grown with extracellular ATP. I would not state that the data support a role for ATP availability regulating aging because human cells do not import ATP from the environment; maybe for yeast or microbes that import ATP unlike normal human cells that make all their own. I would not state that the data support a role for ATP in increasing replicative lifespan since there is no correlation between increased lifespan an increased ATP- rather the opposite is shown - you got no correlation between intracellular ATP at replicative lifespan. What you show is that you can increase replicative lifespan when you increase extracellular ATP in a way that doesn't matter if you further increase intracellular ATP with NTT1, but maybe you were saturated. But then, intracellular ATP doesn't correlate either. So, it doesn't make sense to say that the paper shows that increasing ATP increases replicative lifespan. You gave the cells what seems like an osmotic stress and it didn't even matter if they had the mitochondria to respond. Lifespan didn't correlate with more ATP at all. So, it seems incorrect to indicate that ATP regulates lifespan in yeast, when you haven't shown that it just wasn't osmotic stress and you have shown that it doesn't really have anything to do with how much ATP each cell has. Rather, it seems more like it's the robustness of some other response to the ATP and not how much they imported.

      The authors point out in their rebuttal that, paraphrased, with the QUEEN reporter gene that quantifies ATP via ratiometric fluorescence, some cells increase atp with age, some cells decrease it and others show no age related changes, so it would not be meaningful to make an ATP measurement in old cells. Essentially, that line of reasoning further supports that, while adding extracellular atp seems to increase replicative lifespan, the lack of correlation between intracellular ATP and replicative aging suggest that they are eliciting a stress response to having high extracellular ATP. The increased replicative lifespan seems to be caused by some kind of stress response to the extracellular ATP, possibly osmotic. So, this is an interesting result that suggests that there are mechanisms to respond to high extracellular ATP that result in increased replicative lifespan that aren't caused by the ATP per say, but by having a high concentration of ATP, outside the cell. This paper seems like it might relate to the Kaeberlein paper about high osmolarity increasing replicative lifespan, but I don't know if this condition qualifies as high osmolarity.

      As an aside, this seems like a better model for increased tumor cell growth in the presence of increased extracellular ATP, which happens in some cancers.

      Strengths:

      Longitudinal imaging of single cells. Analyzed ATP levels with two approaches. Creative approach to use NTT1 transporter to increase intracellular ATP levels (that did nothing for lifespan). Solid replicative lifespan data. Seemingly clear lack of correlation between ATP and replicative lifespan among individual cells strongly pointing so some kind of stress response, possibly some kind of osmolarity response like Kaeberlein 2002.

      Weaknesses/inconclusive data:

      The LCMS data seems better than the microscopy data on ATP because it does not suffer from signal interference/befuddlement from autofluorescence or pH or age-related changes in either of those parameters. The authors seem to have subtracted an average autofluorescence from each cell in their revised analysis, but it is unclear if this value changes with age to account for age related changes in autofluorescence; it is also unclear if signal attenuation as a function of age-related intracellular pH change was accounted for. They may have age-related values for autofluorescence or age-related changes in intracellular pH that affect fluorescence of GFP if they go back to their raw data, which might be useful for signal cleanup.

    4. Author response:

      The following is the authors’ response to the original reviews.

      Summary of the Key Clarification Underlying This Revision

      Both reviewers independently identified what appeared to be a direct contradiction between our LC-MS/MS measurements of intracellular ATP (Figure 2) and our single-cell QUEEN reporter measurements of intracellular ATP (Figure 5): NTT1 expression appeared to lower rather than raise intracellular ATP, and exogenous ATP treatment appeared to further lower the QUEEN signal even though LC-MS/MS showed the opposite. We have carefully reviewed the reviewers’ comments alongside our original figures and legends, and we agree that the source of this apparent contradiction was insufficiently explained in the original submission.

      To make the reported quantity intuitively track ATP abundance, we have now:

      - Plotted the reciprocal of the 488 nm signal (1/488 nm) in the updated Figure 5, eliminating the inverse correlation between AU values and ATP abundance;

      - Revised the Figure 5 legend, axis labels, Methods, and Results to explicitly state that the plotted value is directly proportional to intracellular ATP abundance, removing any ambiguity about directionality;

      - Subtracted autofluorescence from the raw QUEEN reporter fluorescence signal;

      - Expanded the Methods and Results to explain, side by side, why the QUEEN-based and LC-MS/MS based measurements are complementary but not numerically identical (different dynamic ranges; live single-cell sampling over time versus bulk lysates at fixed timepoints); and

      - Re-examined Figures 2, 5, and 7 together to confirm that, once the QUEEN directionality is correctly read, the two datasets are internally consistent: NTT1 expression lowers baseline intracellular ATP relative to Wt, and exogenous ATP supplementation raises intracellular ATP in NTT1 cells (and, more modestly and via a distinct mechanism, in Wt cells), by both methods.

      Because this misunderstanding affected interpretation of the foundational ATP data, it appears to have influenced evaluation of all downstream conclusions. For example, neither reviewer meaningfully engaged with:

      - The identification of distinct cell death trajectories of aged cells;

      - The mitochondrial dependency of NTT1-associated toxicity and the observed lifespan differences; and

      - The distinction between intracellular ATP manipulation and extracellular ATP sensing mechanisms.

      Example of a Data Discrepancy Resolved by This Clarification

      In some instances, we think the original figures did not present the data clearly enough to support the intended reading, which we address below.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The authors aim to engineer a synthetic system for manipulating ATP homeostasis in budding yeast by expressing the microsporidian nucleotide transporter NTT1, thereby enabling ATP import from the extracellular environment. Using this system, they attempt to test whether intracellular ATP abundance causally regulates replicative lifespan and whether extracellular ATP sensing contributes independently to longevity pathways. The manuscript presents data from ATP biosensing, transcriptomics, mitochondrial perturbations, and microfluidic aging assays to build a dual-mechanism model linking ATP availability, MAPK signaling, mitochondrial function, and aging trajectories.

      Strengths:

      A major strength of the study is its creative application of xenotopic synthetic biology to directly manipulate ATP homeostasis-an ambitious approach that addresses an important and difficult question in aging biology. The use of complementary methods, including single-cell ATP reporters, microfluidic lifespan measurements, and RNA-seq, generates a rich experimental dataset with the potential to reveal multiple layers of ATP-dependent physiological regulation. The manuscript also raises interesting hypotheses regarding extracellular nucleotide sensing and HOG/MAPK pathway involvement, opening conceptual space for future exploration of ATP-based signaling in yeast.

      Weaknesses:

      Despite these strengths, the manuscript suffers from several critical weaknesses that undermine the central conclusions. Foremost, the intracellular ATP measurements contradict key interpretations: NTT1 expression lowers ATP levels, yet multiple sections assert or assume that NTT1 increases intracellular ATP via import. This unresolved contradiction propagates throughout the mechanistic model. The authors do not consider or experimentally address the more parsimonious explanation that NTT1 may be a bidirectional ATP transporter, which would unify many perplexing results. Several important analyses are missing (e.g., transcriptomic comparison of NTT1 cells with vs. without ATP), and key signaling claims lack proper validation (e.g., Hog1 quantification, AMPK controls). Additionally, inconsistencies in figures-such as incorrect scale bars, mismatched ATP measurements, and a conceptual model contradicted by the data-further detract from clarity. As a result, the manuscript does not yet convincingly achieve its stated aims, and the current evidence does not adequately support the proposed causal relationships between ATP homeostasis and lifespan.

      We thank the reviewer for a rigorous and constructive critique. The central objection that our intracellular ATP measurements contradict our interpretation arose from a presentational error in how the QUEEN reporter data were plotted. As described in our Methods (QUEEN reporter section), the QUEEN biosensor is a ratiometric reporter whose 410 nm/488 nm excitation ratio increases with ATP binding. Because repeated 410 nm excitation causes phototoxicity over the multi-day time course of a replicative aging experiment, our longitudinal single-cell measurements (Figure 5) used only the 488 nm channel, for which fluorescence intensity decreases as ATP binds the reporter. In the original Figure 5, arbitrary fluorescence units (AU) were therefore inversely correlated with intracellular ATP abundance, such that higher AU values corresponded to lower ATP levels. We described this inverse relationship in the Results section and labeled the relevant figure panels “Low versus High,” but on reflection we agree this was not stated clearly enough in the figure legend itself, where a reader would most naturally look for it. As a result, the two datasets read as contradictory even though they are fully consistent, and we take responsibility for that lack of clarity.

      The reviewer proposes that NTT1 may function as a bidirectional transporter, with net ATP export accounting for the reduced ATP we observe in untreated NTT1-expressing cells. We considered this possibility, and our expanded metabolite data argue against it. LC-MS/MS shows that ATP, ADP and AMP are all significantly reduced in untreated NTT1-expressing cells relative to wild type (new Figure S2B), with AMP most severely affected a contraction of the entire adenylate pool rather than a shift in its distribution.

      Our transcriptome data identify the source of this contraction. NTT1 expression produces a coordinated downregulation of de novo purine biosynthesis, spanning the pathway (new Figure S2A), consistent with the pronounced AMP deficit. Parallel pyrimidine biosynthesis genes are unaffected, indicating a purine-specific response rather than a general biosynthetic shutdown. We therefore conclude that reduced de novo purine synthesis, not reverse transport, is the parsimonious explanation for the lower baseline nucleotide levels in NTT1-expressing cells, and we have added this analysis and interpretation to the Results and Discussion.

      Under ATP-replete conditions, both LC-MS/MS and the corrected QUEEN readout show a clear increase in intracellular ATP in NTT1-expressing cells, consistent with import. We have additionally added the transcriptomic comparison of NTT1-expressing cells with and without exogenous ATP that the reviewer requested (new Figures S1–S3), quantified Hog1 phosphorylation, and audited every figure, legend and numerical value in the manuscript.

      Reviewer #2 (Public review):

      Summary:

      This work presents interesting findings where the addition of exogenous ATP extends the replicative lifespan of yeast cells in a way that seems uncorrelated with actual increased intracellular ATP levels or mitochondria. To be clear, the addition of ATP to yeast growth media increases the number of cell divisions per cell in yeast. Expression of the NTT1 ATP transporter gene increases intracellular ATP levels according to LCMS analysis, but the effect on replicative lifespan works without the NTT1 gene and without an intracellular increase in ATP (possibly with a decrease in intracellular ATP), so the effect appears to be independent of the effect on intracellular ATP levels or mitochondria, as mitochondria-less R0 yeast cells also have increased numbers of cell division when grown with extracellular ATP. The plots in Figure 5 make it seem like exogenous ATP addition lowers intracellular ATP for both the NTT1 cells and the wild-type cells, and that is not what the data in Figure 2d with LCMS shows.

      As an aside, this seems like a better model for increased tumor cell growth in the presence of increased extracellular ATP, which happens in some cancers.

      Restated, the data suggest they were successful in increasing intracellular ATP by LCMS, but not by queen reporter, and that the seemingly likely increased intracellular ATP was not causative, as cells that did not have an increase in intracellular ATP, but had the same exogenous ATP addition, also gained an increase in replicative lifespan. There could also be two distinct mechanisms extending replicative lifespan to the same degree in these two different strains. More measurements, controls, and analyses are needed to accurately determine what is happening with intracellular ATP levels with age. It is currently unknown if there is any correlation between ATP levels and replicative aging (with properly controlled longitudinal measurements).

      Strengths:

      Longitudinal imaging of single cells. Analyzed ATP levels with two approaches. Creative approach to use NTT1 transporter to increase intracellular ATP levels. Solid replicative lifespan data.

      Weaknesses:

      Mostly unclear about ATP levels with age and the relationship, or lack thereo,f between intracellular ATP levels and replicative lifespan. No idea what this effect depends on, but some ideas what it does not depend on (mitochondria or increased intracellular ATP). Experiments seem to lack biological controls (cells without gfp) for age related changes in autofluorescence (and pH that can affect gfp signal) for the fluorescent microscopy quantifying ATP with age using the QUEEN reporter (seems that way as written); conflicting evidence on ATP levels; lack of LC-MS measurements in old cells; no apparent correlation between ATP levels and replicative lifespan, but that could be wrong - just not apparent from the longitudinal data plots. The LCMS data seems better than the microscopy data on ATP because the microscopy approach seems to lack proper biological controls, and the selection of only the top 40% of pixels to quantify signal seems unjustified as written, and possibly prone to technical artifacts. Figure 2 B&C plots of ATP levels should show what the cells were normalized to. The figures also seem too diluted and should probably be combined or put in the supplements (hog1 western) if they do not relate to the lifespan effect. There seem to be some technical scientific editorial errors, like in Figure 7.

      We appreciate this careful and, in most respects, accurate reading. The reviewer concludes that the lifespan extension conferred by extracellular ATP does not depend on an increase in intracellular ATP, since it occurs in cells lacking NTT1 and in rho<sup>0</sup> cells, and suggests that two distinct mechanisms may extend lifespan in the two strains. This is precisely the model our data supports, and we are encouraged that the reviewer arrived at it independently: we propose an intracellular, import-dependent and mitochondria-dependent mechanism operating in NTT1-expressing cells, and a separate extracellular ATP-sensing mechanism that acts without import and without functional mitochondria. We have rewritten the Results and Discussion to make this dual-mechanism structure explicit rather than leaving it to be inferred, and Figure 8 has been revised so that the model matches the data.

      The apparent conflict between the QUEEN and LC-MS/MS measurements, which underlies much of the reviewer's uncertainty about ATP levels with age, is resolved by the correction described below. We have additionally subtracted autofluorescence from the reporter signal, stated the normalization for the ATP measurements in the Figure 2 legends, and added a justification in the Methods for restricting quantification to the brightest pixels. The QUEEN reporter is a soluble cytoplasmic protein excluded from the vacuole, which occupies an increasing fraction of cell volume in ageing mother cells, so whole-cell quantification systematically underestimates cytoplasmic signal in old cells for reasons unrelated to ATP. On the request for LC-MS/MS on sorted old cells, we agree this would be valuable in principle, but because individual cells follow divergent ATP trajectories with age, a bulk measurement of a sorted population would average across those trajectories and would not be directly comparable to our single-cell data.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      The intracellular ATP measurements contradict key interpretations: NTT1 expression lowers ATP levels, yet multiple sections assert or assume that NTT1 increases intracellular ATP via import. This unresolved contradiction propagates throughout the mechanistic model. The authors do not consider or experimentally address the more parsimonious explanation that NTT1 may be a bidirectional ATP transporter… Several important analyses are missing (e.g., transcriptomic comparison of NTT1 cells with vs. without ATP), and key signaling claims lack proper validation (e.g., Hog1 quantification, AMPK controls). Additionally, inconsistencies in figures, such as incorrect scale bars, mismatched ATP measurements, and a conceptual model contradicted by the data, further detract from clarity.

      We thank the reviewer for this central and constructive critique. As explained above, the apparent contradiction in intracellular ATP directionality stemmed from an underexplained sign convention in the QUEEN reporter readout in Figure 5, which we have now corrected and clarified throughout the text, legends, and axis labels. With this correction, NTT1 cells show reduced baseline ATP and a clear, dose-dependent increase in intracellular ATP upon exogenous ATP treatment, consistent between the QUEEN and LC-MS/MS assays. There is a discrepancy between the two methods (QUEEN versus LC-MS/MS) specifically in the Wt cell ATP measurements, which we discuss in the Results section.

      The authors seem to assume that NTT1 expression should result in ATP influx… the authors should consider an alternative interpretation: the ATP transporter is bidirectional… this interpretation seems to be consistent with many of the observations in the manuscript, and this bidirectional ATP flux can be experimentally tested.

      We thank the reviewer for this insightful alternative hypothesis. With the QUEEN directionality now corrected (see summary above), the core observation motivating this suggestion lower ATP in NTT1 cells alongside evidence of import is substantially resolved: NTT1 cells have lower baseline intracellular ATP than Wt, and both LC-MS/MS and the corrected QUEEN readout show that exogenous ATP raises intracellular ATP specifically in NTT1 cells, consistent with net import under ATP-replete conditions. In addition, our expanded LCMS/MS data (Figure S2) suggest that NTT1 overexpression results in decreased de novo purine biosynthesis, offering a possible explanation for the decreased ATP levels in these cells.

      The RNA-seq analysis compared the effects of adding exogenous ATP in WT and NTT1 cells. However, analysis can also be done to compare the effects of NTT1 under conditions with or without exogenous ATP.

      We have added the missing transcriptomic comparison of NTT1 cells with versus without exogenous ATP, and the corresponding data are now presented in three new supplementary figures (Figures S1–S3).

      “In NTT1-expressing cells, ATP is directly imported from the extracellular environment…” the authors are assuming NTT1 cell imports ATP from media with ATP supplementation. This should be reconciled with the lower intracellular ATP concentration and lifespan shortening.

      This statement refers specifically to the ATP-treated condition, in which both LC-MS/MS and the corrected QUEEN measurements show elevated intracellular ATP in NTT1 cells relative to untreated NTT1 cells and to Wt. We have revised the text to make this conditional context explicit.

      The western blots for Hog1p and Hog1 should be quantified… I suggest using p-Hog1 or phos-Hog1 to indicate the phosphorylated form.

      We have now quantified Hog1 phosphorylation by western blot analysis from two independent biological replicate experiments, each probed with a different commercial phospho-Hog1 antibody, to confirm that the result is not antibody-specific (Figure 4, Figure S8; Methods). We have also adopted the reviewer’s suggested nomenclature and now consistently denote the phosphorylated form as Hog1ᵖ throughout the text and figures to avoid ambiguity with the Hog1p gene product naming convention.

      The model in Figure 8 indicates a larger lifespan extension in the NTT1 cells. This is not consistent with the data presented in Figure 5-7.

      The statement regarding a larger lifespan extension is consistent with the data presented in Figures 5 and 7, but we agree this connection was not spelled out clearly enough in the original text. These two figures differ in their treatment conditions. In Figure 6D, 10 µM ATP treatment resulted in a significant median lifespan extension in both Wt and NTT1 cells, though the magnitude of the extension differed: As stated in the Results, Wt cells showed a median RLS increase of 41% (p = 1.28E-12) upon 10 µM ATP supplementation, while NTT1 cells, which also showed an increase in ATP levels (Fig. 6B, C), showed an even more pronounced extension (median RLS increase = 60%, p = 4.03E-18) in lifespan (Fig. 6D). In Figure 7, we analyzed the higher dose of ATP treatment (10 mM) to examine its toxic effect on lifespan. Figure 7A shows that the lifespan-extension effect observed at the low ATP dose (Fig. 6) diminished under the 10 mM treatment condition for NTT1 cells but not for Wt cells. Figure 7B shows that elimination of functional mitochondria rescues this mitochondrial toxicity in NTT1 cells, such that these cells extend their lifespan to a magnitude comparable to Wt under 10 mM ATP treatment conditions.

      Reviewer #2 (Recommendations for the authors):

      The fluorescence imaging shows that cells with exogenous ATP have lower ATP levels, and that is not what was shown in Figure 2 by LCMS. So, as it is, this is confusing and not conclusive and may have to do with technical artifacts.

      We would like to clarify that our data do not in fact conflict. Figure 2B (fluorescence imaging) shows that NTT1 cells have lower intracellular ATP than Wt under baseline (no exogenous ATP) conditions, and that exogenous ATP treatment increases intracellular ATP abundance in both cell types. Figure 2C shows that under 2-deoxyglucose (2DG) treatment, Wt cells display decreased ATP levels regardless of exogenous ATP treatment, whereas NTT1 cells show increased intracellular ATP upon ATP treatment, further supporting the import function of NTT1. Our LC-MS/MS data (Fig. 2D, left panel) independently corroborate the fluorescence results in Figure 2B: NTT1 cells show lower ATP abundance than Wt cells at baseline, and exogenous ATP treatment raises intracellular ATP in NTT1 cells but not in Wt cells. We discuss the modest discrepancy between the LC-MS/MS and QUEEN measurements specifically for Wt cells in the Results section.

      The plots in Figure 5 make it seem like exogenous ATP addition lowers intracellular ATP for both the NTT1 cells and the wild-type cells, and that is not what the data in Figure 2d with LCMS shows… Restated, the data suggest they were successful in increasing intracellular ATP by LCMS, but not by queen reporter, and that the seemingly likely increased intracellular ATP was not causative…

      We appreciate the reviewer’s detailed reading of Figures 2 and 5. As described above, this apparent conflict arose because Figure 5 originally plotted a raw fluorescence value that is inversely related to ATP abundance without stating this clearly enough in the legend. We have corrected the legend, axis labels, and Results text so that the plotted quantity in Figure 5 (1/488 nm QUEEN signal) increases with ATP abundance, matching the LC-MS/MS data in Figure 2. With this correction, both assays agree that exogenous ATP treatment raises intracellular ATP in NTT1 cells; we discuss in the Results the more modest and assay-dependent behavior we observe in Wt cells, where LC-MS/MS and QUEEN differ somewhat in sensitivity and time-resolution.

      It needs a bit more experimental clarification and some thinking about what it means to disrupt ATP homeostasis with high extracellular levels of ATP, a generally biologically unnatural/pathological situation.

      The central premise of our study is that altered intracellular ATP is associated with lifespan. Mitochondrial functional decline is a hallmark of ageing, but whether this correlates with decreased ATP, and whether age-associated ATP changes are linked to cellular aging, remains an overlooked question. Here, for the first time, we show that not every cell exhibits a decreasing ATP trajectory with age, and that maintaining ATP homeostasis can extend lifespan. Interestingly, extracellular ATP sensing is directly relevant to cell-to-cell communication (purinergic signaling); however, this has never been characterized in yeast. Our data indicate that a similar, conserved mechanism may also be present in yeast, although the exact components of this sensing cascade require further study. This information is included in the Introduction and Discussion sections of the manuscript.

      The data for ATP levels with age needs to be cleaned up/refined… Sort some high bud scar cells and test them with LCMS… run wild-type cells to subtract out the autofluorescence… or redo it on a scope with linear unmixing.

      Our data indicates that each cell follows a different ATP-abundance trajectory during aging: while some cells show decreasing ATP with age, others show no change, and still others show increasing ATP. Population-level testing of sorted old cells would therefore not yield results that are directly comparable to our microfluidic experiments, in which we track individual cell trajectories over time. In the updated figure, we have subtracted autofluorescence.

      Figures 1 and 2 could be combined, and so could 3 and 5, 6 and 7. Figure 4 should probably be a supplement or part of another figure since upregulating HOG1 has nothing to do with the lifespan extension effect.

      We appreciate this suggestion for streamlining the figures. Our data show only decreased Hog1 activity under ATP treatment conditions; we did not test whether this decline in Hog1 activity is associated with lifespan extension. However, we would like to include this figure in the main text, because it directly validates the effect of extracellular ATP sensing on modulation of Hog1 activity.

      Figure 7 seems like it is showing that the NTT1 strain’s shorter replicative lifespan is rescued by losing mitochondria, and not that toxicity from ATP in the NTT1 is alleviated by losing mitochondria.

      Both the NTT1 strain’s shorter replicative lifespan and its ATP toxicity (10 mM) are rescued by eliminating respiratory mitochondria. In Figure 7, we analyzed the higher dose of ATP treatment (10 mM) to examine its toxic effect on lifespan. Figure 7A shows that the lifespan-extension effect observed at the low ATP dose (Fig. 5) diminished under the 10 mM treatment condition for NTT1 cells but not for Wt cells. Figure 7B shows that elimination of functional mitochondria rescues this mitochondrial toxicity in NTT1 cells, such that these cells extend their lifespan to a magnitude comparable to Wt under 10 mM ATP treatment conditions.

      I think you should make sure your pinhole is opened up to catch all the signal in z from each cell… It seems like taking the mean intensity of the top 40% of pixels in a cell can create technical artifacts… Please justify why you use that approach.

      We have added justification for the top-40%-pixel approach to the Methods: because the QUEEN reporter is a soluble cytoplasmic protein excluded from the large vacuoles that occupy an increasing fraction of cell volume as mother cells age, whole-cell or full-z-stack quantification systematically underestimates cytoplasmic ATP signal in older cells for reasons unrelated to ATP abundance. Restricting analysis to the brightest pixels focuses quantification on reporter-containing cytoplasm and avoids this age-confounded dilution artifact.

    1. eLife Assessment

      This important large-scale comparative study of odorant receptor (OR) genes across more than 100 insect species, combining sequence- and structure-based approaches, represents a significant contribution in the exploration of the evolution of this large gene family involved in the detection of odorant signals by olfactory neurons. Most notably, the authors uncover a structural feature unique to the odorant receptor co-receptor Orco that reduces ligand binding affinity. From this solid data emerges a general integrative framework to apprehend the diversity and evolution of odorant receptor genes in insects.

    2. Reviewer #1 (Public review):

      [Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. All the reviewers' comments have been adequately addressed and taken into account.]

      Objectives of the study and impact of the work

      The authors of this article primarily aim to reconstruct the evolutionary history of the insect odorant receptor (OR) family, which is responsible for the detection of odorant signals by olfactory neurons. Due to the lack of phylogenetic signal present in the sequences of this multigene family, which evolves very rapidly, phylogenetic analyses have so far never made it possible to precisely retrace how ORs diversified prior to the appearance of present-day insect orders, and what the drivers of this diversification were. For example, one may suspect that the adaptation of ORs to odors emitted by plants constituted a critical step in insect evolution during the "angiosperm terrestrial revolution," which occurred at the end of the Cretaceous, but nothing currently allows this to be asserted.

      There are very nice examples, notably in drosophilids, derived from comparisons between closely related species and documenting mechanisms of OR adaptation to certain signals. However, what the authors attempt to do in this work is to produce a macroevolutionary analysis at the scale of insects as a whole, based almost exclusively on bioinformatic analyses. To do this, they annotated OR genes in about one hundred insect species and developed pipelines for analyzing sequence similarity, structural similarity and functional similarity, the latter being estimated through a molecular docking approach. An important element in the evolution of insect ORs is the appearance of a unique co-receptor, called Orco, which appears to be an OR that has lost the ability to bind odorants. In addition to the large-scale bioinformatic analysis, the authors also aim to explore more specifically the factors that favored the emergence of Orco and the selective advantage conferred by the existence of OR-Orco complexes.

      Given the importance of odorant receptors in insect biology and in their adaptation to different environments and lifestyles, retracing their evolutionary history is indeed a major question in evolutionary biology. In principle, this type of work therefore has the potential to become a reference in the field and to provide a basis for significant scientific advances.

      Major strengths and weaknesses

      The sampling chosen for collecting OR sequences is very impressive, with more than 100 insect families represented, covering most of the major orders. This sampling appears appropriate for the question being addressed. The analysis pipeline used to collect the sequences makes sense, relying on homology-based annotation tools coupled with a structure-based filter. Nevertheless, one can note aberrant numbers of ORs for certain species (much lower than reality). A lower number of OR genes is somewhat expected, as the authors chose to apply a fairly stringent filter on sequence quality (based on predicted 3D structure), which reduces the number from 14,000 to 9,000. This choice seems logical given the subsequent use of these data, but it inevitably leads to data loss. However, the low number of genes also results from the fact that the pipeline did not function correctly for all genomes.

      In the revised version of the manuscript, the authors included a benchmarking step, which is a good point. They compared their OR gene annotations with previous reports in the same species. Unfortunately, this comparison is irrelevant because the chosen reference OR repertoires are actually a mix of transcriptome and genome annotations. Furthermore, comparisons with entire OR gene repertoires essentially demonstrate that their annotation was of good quality for species in which OR sequences were already present in the query OR database, but poor for species in which they were not. Therefore, these supplementary analyses made by the authors are not particularly in favor of an overall good quality of OR gene repertoires in the >110 species studied. The fact that some OR genes may be missing and that the total number may not be exact for each species is not prohibitive for studying the evolution of the family on a broad scale. However, it does call into question the correlation between the number of ORs and lifestyles and diets.

      From the dataset collected, the authors attempted to categorize ORs in several ways, starting with the reconstruction of sequence similarity networks. The approach is interesting, but fails to reveal homology relationships between ORs from species belonging to different insect orders. So, it is unclear what the advantage of this approach is compared with the "classical" phylogenetic approach.

      The clustering based on structure also leads to the identification of a majority of "order-specific" clusters, which does not provide major insights into the evolution of ORs. However, the authors highlight a group of ORs in flies that appear to possess an unusual intracellular region, as well as a cluster of OR shared across many insect orders that exhibit a larger binding cavity. This is really interesting, although more relevant to OR structure than to their evolution.

      The analysis of structural diversity then leads the authors to focus on the Orco co-receptors, which are characterized by modifications of the binding pocket and the appearance of an extracellular loop that could explain the loss of the ability to bind odorant molecules. This part, which relies on in vitro experiments, is interesting and constitutes the most striking result of this study, which could in itself have been the subject of a separate manuscript.

      The rest of the manuscript is based on the prediction of OR response spectra using molecular docking. The work that has been carried out is extremely substantial, and the objective of linking clusters based on sequence similarity or 3D structural similarity with functional categories is entirely relevant. The docking score threshold used was chosen thoughtfully, which is very good, and according to the calculation performed should ensure a true positive rate of more than 20%, which is excellent in such a docking analysis. But in the absence of functional validation, this 20% true positive rate is not sufficient to extrapolate OR function, and docking-derived binding breadth measures used in the remaining of the manuscript have to be taken with caution, as acknowledged by the authors themselves. Consequently, the chance that results of this part of the work will enlighten the evolution of OR on a broad scale is low. For example, the fact that insect lineages that emerged after the Permian-Triassic extinction have more broadly-tuned OR is an interesting observation, yet remains highly speculative.

      In summary, despite the large number of analyses performed, the authors do not really succeed in achieving the stated objective of reconstructing the evolutionary history of insect ORs, and the results obtained do not strongly support all the conclusions regarding the links between OR repertoires and environment or lifestyle.

    3. Reviewer #2 (Public review):

      The remarkable evolvability of the olfactory system enables animals to rapidly adapt to dynamic and chemically complex environments. Over the past two decades, substantial effort has been devoted to uncovering the evolutionary principles that drive the diversification of odorant receptors (ORs), yielding key insights into the forces shaping their striking variability in both vertebrates and insects. In this manuscript, Zhang and colleagues analyze the OR repertoires of over 100 insect species, leveraging sequence and structural similarity to infer patterns of gene family evolution within this diverse and ecologically important clade. By integrating sequence-based and structure-based comparisons, their study builds on a compelling and recently emerging line of research made possible by the advent of AlphaFold, which has previously clarified the phylogenetic relationship between insect Ors and the gustatory receptor gene family and revealed the unexpectedly deep evolutionary origins of this ancient structural fold.

      Applying this approach to a large set of ORs derived from species throughout the insect phylogeny, the authors confirm many previously reported patterns of OR evolution. More importantly, with their large-scale approach the authors found new structural features of different Or clades, resulting in intriguing testable hypotheses about Or evolution that have the potential to support future directions in studying this important gene family. For example, the authors identify a structural feature mostly unique to the OR co-receptor ORco, a beta-sheet in EL2, which they functionally show reduces odorant binding affinity - a key aspect of ORco, which does not bind ligands in the ancestral ligand-biding site. This is a particularly strong part of the manuscript, since the authors support their in silico-derived hypothesis with functional data.

      In an attempt to assess the relationship between sequence identity and structure on one hand and function on the other, the authors perform an in-silico structure prediction and chemical docking analysis. While not suitable to assess the actual function of individual receptors without functional testing, this strategy revealed potential receptor clades with differences in the number and type of docked chemicals. As a response to the previous reviews, the authors now changed their writing to refer to these results as 'docking-derived function' instead of just 'function'. While I appreciate that the authors changed their wording, this semantic change still can be read as referring to docking-derived predictions as conclusions of actual biological function. However, this is not the case. Any functional claims based solely on the docking approach need to be interpreted with caution, as in silico-only approaches are currently not well enough predictors for actual receptor function. Accordingly, I strongly recommend to remove the parts that connect receptor function to structure or ecology.

      Assessing their docking analyses with respect to species ecology, the authors identify correlations suggesting that Or repertoires might follow predictable evolutionary trajectories broadly aligned with species ecology. These are interesting hypotheses. However, due to the inability to predict actual receptor function the author's claims about ecology-function relationships are not well supported. Accordingly, the authors should change the respective parts of the manuscript in way that it is clear these are currently untested hypotheses. This includes adjusting the title, since the work does not find any experimental support for ecological tuning.

      I believe that future studies combining functional testing with an explicit evo-eco framework could benefit from this work and the explicit hypotheses it generates. But the authors should be careful to not overstate their in-silico derived results.

    4. Author response:

      The following is the authors’ response to the previous reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Objectives of the study and impact of the work

      The authors of this article primarily aim to reconstruct the evolutionary history of the insect odorant receptor (OR) family, which is responsible for the detection of odorant signals by olfactory neurons. Due to the lack of phylogenetic signal present in the sequences of this multigene family, which evolves very rapidly, phylogenetic analyses have so far never made it possible to precisely retrace how ORs diversified prior to the appearance of present-day insect orders, and what the drivers of this diversification were. For example, one may suspect that the adaptation of ORs to odors emitted by plants constituted a critical step in insect evolution during the "angiosperm terrestrial revolution," which occurred at the end of the Cretaceous, but nothing currently allows this to be asserted.

      There are very nice examples, notably in drosophilids, derived from comparisons between closely related species and documenting mechanisms of OR adaptation to certain signals. However, what the authors attempt to do in this work is to produce a macroevolutionary analysis at the scale of insects as a whole, based almost exclusively on bioinformatic analyses. To do this, they annotated OR genes in about one hundred insect species and developed pipelines for analyzing sequence similarity, structural similarity and functional similarity, the latter being estimated through a molecular docking approach. An important element in the evolution of insect ORs is the appearance of a unique co-receptor, called Orco, which appears to be an OR that has lost the ability to bind odorants. In addition to the large-scale bioinformatic analysis, the authors also aim to explore more specifically the factors that favored the emergence of Orco and the selective advantage conferred by the existence of OR-Orco complexes.

      Given the importance of odorant receptors in insect biology and in their adaptation to different environments and lifestyles, retracing their evolutionary history is indeed a major question in evolutionary biology. In principle, this type of work therefore has the potential to become a reference in the field and to provide a basis for significant scientific advances.

      Major strengths and weaknesses

      The sampling chosen for collecting OR sequences is very impressive, with more than 100 insect families represented, covering most of the major orders. This sampling appears appropriate for the question being addressed. The analysis pipeline used to collect the sequences makes sense, relying on homology-based annotation tools coupled with a structure-based filter. Nevertheless, one can note aberrant numbers of ORs for certain species (much lower than reality). A lower number of OR genes is somewhat expected, as the authors chose to apply a fairly stringent filter on sequence quality (based on predicted 3D structure), which reduces the number from 14,000 to 9,000. This choice seems logical given the subsequent use of these data, but it inevitably leads to data loss. However, the low number of genes also results from the fact that the pipeline did not function correctly for all genomes.

      In the revised version of the manuscript, the authors included a benchmarking step, which is a good point. They compared their OR gene annotations with previous reports in the same species. Unfortunately, this comparison is irrelevant because the chosen reference OR repertoires are actually a mix of transcriptome and genome annotations. Furthermore, comparisons with entire OR gene repertoires essentially demonstrate that their annotation was of good quality for species in which OR sequences were already present in the query OR database, but poor for species in which they were not. Therefore, these supplementary analyses made by the authors are not particularly in favor of an overall good quality of OR gene repertoires in the >110 species studied. The fact that some OR genes may be missing and that the total number may not be exact for each species is not prohibitive for studying the evolution of the family on a broad scale. However, it does call into question the correlation between the number of ORs and lifestyles and diets.

      Thank you for this important comment. We agree that it is inappropriate to directly compare genome-based OR annotations with OR repertoires derived solely from transcriptomes, because transcriptome-only datasets generally do not capture the complete OR gene complement of a species. We therefore repeated the benchmarking analysis and collected, wherever available for the species included in this study, published OR repertoires based on genome annotation or genome annotation supported by transcriptomic evidence. Transcriptome-only OR datasets are no longer included in this comparison.

      The updated benchmarking analysis shows that, for most species with comparable genome-based reference repertoires, the number of ORs identified in this study exceeds 70% of the previously reported number, with good agreement in OR repertoire size for most species (Table S1). Diabrotica virgifera is an exception: a previous manual annotation reported 124 complete ORs, whereas we identified 78, approximately two-thirds of the previously reported number. This result indicates that our automated annotation and stringent structural filtering pipeline may indeed underestimate OR repertoire size in some species. The large, repeat-rich genome of this species may further complicate gene prediction and the reconstruction of OR loci. We have therefore explicitly stated in the revised manuscript that incorporating transcriptomic evidence and manual curation may help recover more complete OR repertoires from such complex genomes.

      Another notable outlier is Anagrus nilaparvatae. A previous study reported 88 ORs, whereas we identified 330. Examination of the methods used in that study revealed that its OR search relied primarily on homology searches using known OR sequences from two other hymenopteran species. Because insect ORs constitute a rapidly evolving gene family with pronounced lineage-specific expansions, a limited query set may fail to detect highly divergent or species-specific ORs. In comparison, our study used OR queries from a broader range of insects and a genome-level annotation pipeline specifically designed for insect ORs, which may have enabled the detection of more highly divergent candidate ORs. Because the protein sequences of the 88 ORs were not made available in the original study, we are currently unable to perform a one-to-one comparison to verify whether all of these ORs are included in our annotations.

      You also noted that our better performance in species such as Drosophila melanogaster, Anopheles gambiae, and Bombyx mori might partly reflect the inclusion of known OR sequences from these species in the query database. We consider this an important and directly testable question. We therefore divided the benchmark species into two groups according to whether conspecific OR sequences were represented in the query database, and used ln(OR count_this study / OR count_previous) to quantify differences in OR counts relative to previous genome-based annotations. No significant difference was detected between the two groups (P = 0.841, Fig. S1 B). Given the unusually large difference in OR counts between the two studies for A. nilaparvatae, we also performed a sensitivity analysis excluding this species, which yielded the same conclusion (P = 0.413, Fig.S1 C). Thus, among the species for which comparable genome-based reference repertoires are currently available, we found no evidence that the inclusion of conspecific ORs in the query database systematically affected the final OR repertoire size. We have added this analysis to the supplementary material of the revised manuscript.

      Finally, we agree that annotation biases in OR repertoire size may affect analyses of its correlations with ecological traits. We therefore repeated all relevant ecological sensitivity analyses using an updated OR count dataset, taking the larger of the OR counts from our study and the previous annotation for each species (Fig. S1 D- G). The main statistical results remained consistent with the original analyses: OR counts were still significantly associated with adult diet, larval diet, and habitat, whereas no significant association was detected with circadian rhythm. We therefore consider the statistical associations between OR counts and the main ecological variables to be reasonably robust within the range of annotation discrepancies detectable using the currently available genome-based benchmarks, although automated annotation and stringent structural filtering may underestimate OR counts in some species. The updated benchmarking analyses are described in Supplementary Text S1 and summarized in Table S1.

      The following revision has been added (lines 103-107): “Benchmarking against curated OR repertoires showed that the automated pipeline recovered most previously reported ORs and that stringent structural filtering produced a high-confidence set of structurally intact receptors (Supplementary Text S1, Tables S1–S3, and fig. S1B–C). For most species with comparable genome-based references, retained OR counts exceeded 70% of published values.”

      From the dataset collected, the authors attempted to categorize ORs in several ways, starting with the reconstruction of sequence similarity networks. The approach is interesting, but fails to reveal homology relationships between ORs from species belonging to different insect orders. So it is unclear what the advantage of this approach is compared with the "classical" phylogenetic approach.

      Thank you for raising this important question. Our intention was not to replace classical phylogenetic analysis with sequence similarity networks. Rather, we used network-based approaches as a class-wide framework to classify ORs independently according to sequence similarity, structural similarity, and docking profiles, and then compared how these different classifications correspond across insect taxa and major macroevolutionary transitions.

      For smaller gene families with limited sequence divergence, defining subfamilies based on gene trees is a well-established and highly effective approach, because phylogenetic trees can both identify subfamilies and reveal their evolutionary relationships. However, the insect OR superfamily exhibits pronounced birth-and-death evolution, with frequent gene duplications and losses across lineages, and comprises a vast number of highly divergent sequences. Consequently, when a single phylogenetic tree is constructed for the entire OR superfamily, deep nodes often lack sufficient support, and relationships among subfamilies may be difficult to resolve consistently. It is therefore challenging to obtain a stable and consistent classification of thousands of ORs into subfamilies across insects using a single gene tree alone. We therefore used sequence similarity networks as a complementary classification approach. Their main advantage is that they allow large datasets to be grouped according to sequence similarity among all ORs without requiring the resolution of every deep phylogenetic relationship. More importantly, similar network frameworks can be used to construct sequence similarity, structural similarity, and docking-profile similarity networks, allowing SeqC, StrC, and dPC to be compared directly within a common analytical framework. This is difficult to achieve using conventional gene trees alone and is the main reason for our choice of a network-based approach.

      Sequence similarity networks alone cannot directly resolve homology relationships among ORs from different insect orders. During the previous revision, we therefore sought to link sequence communities to phylogenetic relationships. We conducted additional phylogenetic analyses of shared sequence communities containing multiple insect orders to search for potentially conserved OR lineages spanning different orders. However, among these shared communities, we identified only one conserved OR lineage spanning four insect orders with relatively clear phylogenetic support. We have added this result to the Supplementary Data S6. This finding further indicates that clearly identifiable orthology relationships spanning multiple orders are very limited among extant insect ORs.

      The following revision has also been added (lines 150-154): “The SSN is not intended to replace phylogenetic reconstruction, but rather to provide a complementary framework for summarizing sequence relationships among thousands of highly divergent ORs at a class-wide scale. It allows major patterns of OR sequence similarity and diversification to be visualized more clearly across the entire dataset. Phylogenetic analyses can then be used to further resolve evolutionary relationships within specific sequence communities.”

      The clustering based on structure also leads to the identification of a majority of "orderspecific" clusters, which does not provide major insights into the evolution of ORs. However, the authors highlight a group of ORs in flies that appear to possess an unusual intracellular region, as well as a cluster of OR shared across many insect orders that exhibit a larger binding cavity. This is really interesting, although more relevant to OR structure than to their evolution.

      Thank you for this important comment. We agree that the previous version of the manuscript focused primarily on structural differences among structural communities and did not adequately explore their potential evolutionary implications. StrC23 is the most widely distributed structural community, spanning 12 insect orders and containing 46.3% of the ORs. We further found that its putative binding pockets were significantly larger than those of other ORs, which we previously interpreted as a potentially conserved structural pattern across taxa. Following your suggestion, we conducted additional analyses of the phylogenetic and orthology relationships within StrC23. The results showed that StrC23 does not correspond to a single, broadly conserved orthogroup spanning insect orders, but instead contains 156 distinct orthologous groups. Of these, 24 encompass more than 10 species. Only a few of these larger groups span multiple insect orders: one contains ORs from Hymenoptera, Diptera, and Trichoptera, and two contain ORs from both Lepidoptera and Trichoptera. The remaining groups show pronounced order specificity, including nine Hymenoptera-specific groups, eight Lepidoptera-specific groups, and four Diptera-specific groups.

      These results indicate that the large putative binding pocket shared by StrC23 members cannot simply be attributed to a single conserved orthologous lineage spanning multiple orders, but is distributed across several phylogenetically independent OR lineages. This pattern may reflect convergent evolution toward similar structural features in different OR lineages, or the retention of an older structural feature in multiple OR lineages that subsequently underwent substantial sequence divergence. The current data do not allow us to distinguish conclusively between these two possibilities. We have added the phylogenetic and orthologous-group analyses of StrC23 to the revised manuscript and supplementary material to further clarify the evolutionary implications of this structural community shared across insect orders.

      The following revision has been added (lines 208-218): “To further investigate the evolutionary basis of this broadly shared structural feature, we examined the phylogenetic and orthology relationships among StrC23 receptors. StrC23 was divided into 156 orthologous groups, 24 of which contained ORs from more than 10 species. However, only a few of these larger groups spanned multiple insect orders: one included ORs from Hymenoptera, Diptera, and Trichoptera, while two included ORs from both Lepidoptera and Trichoptera (Supplementary Data S6). Thus, StrC23 does not represent a single conserved cross-order orthologous lineage, but instead comprises multiple phylogenetically distinct OR lineages. Its shared large-pocket architecture therefore cannot be explained solely by conservation of a single orthologous lineage and may reflect either structural convergence or retention of an ancient structural feature followed by substantial sequence divergence.”

      The analysis of structural diversity then leads the authors to focus on the Orco co-receptors, which are characterized by modifications of the binding pocket and the appearance of an extracellular loop that could explain the loss of the ability to bind odorant molecules. This part, which relies on in vitro experiments, is interesting and constitutes the most striking result of this study, which could in itself have been the subject of a separate manuscript.

      We thank the reviewer for the positive assessment of this part of our work.

      The rest of the manuscript is based on the prediction of OR response spectra using molecular docking. The work that has been carried out is extremely substantial, and the objective of linking clusters based on sequence similarity or 3D structural similarity with functional categories is entirely relevant. The docking score threshold used was chosen thoughtfully, which is very good, and according to the calculation performed should ensure a true positive rate of more than 20%, which is excellent in such a docking analysis. But in the absence of functional validation, this 20% true positive rate is not sufficient to extrapolate OR function, and docking-derived binding breadth measures used in the remaining of the manuscript have to be taken with caution, as acknowledged by the authors themselves. Consequently, the chance that results of this part of the work will enlighten the evolution of OR on a broad scale is low. For example, the fact that insect lineages that emerged after the Permian-Triassic extinction have more broadly-tuned OR is an interesting observation, yet remains highly speculative.

      Thank you for this important comment. We agree that, even after optimizing the docking threshold, the current hit rate of approximately 20% remains insufficient to interpret molecular docking results as reliable predictions of actual OR ligand responses or functions. We also acknowledge that the biological implications of some docking results were still overstated in the previous revision, potentially leading readers to interpret these computational results as approximations of actual OR function. To avoid this misunderstanding, we have further revised the relevant terminology and interpretation of results throughout the latest manuscript. For results derived entirely from molecular docking, we no longer use terms such as ‘docking-derived function’, ‘functional community’, or other expressions that might imply experimental evidence of function. Instead, we consistently use ‘docking profile’ to make clear that these analyses reflect only similarities in computational docking patterns among receptors.

      In addition, following your comments and the related suggestions from Reviewer 2, we have removed all results and discussion that directly linked docking-based predictions of OR function or recognition breadth to species ecology. We have explicitly presented the few remaining macroevolutionary comparisons as hypothesis-generating observations rather than evidence of functional evolution. For example, differences in docking-profile composition among extant insect lineages that originated before or after the end-Permian mass extinction (EPME) are now described only as a broad statistical pattern, with any interpretation framed as an experimentally untested hypothesis. Whether this pattern reflects actual differences in receptor tuning, and whether it is evolutionarily linked to environmental changes associated with the EPME, still requires testing through broader taxonomic sampling, reliable ancestral-state reconstruction, and functional experiments. We hope that these revisions restrict the interpretation of molecular docking results to what the evidence can support and bring the corresponding conclusions into closer alignment with the current strength of evidence.

      The following revision has been added in Result section (lines 367-369): “These observations should be interpreted as a docking-derived comparative pattern among extant lineages rather than direct evidence of ancestral functional changes across the

      EPME.”

      And Discussion section (lines 410-414): “However, this interpretation remains hypothetical. Because rapid OR evolution and limited cross-order orthology preclude reliable reconstruction of deep ancestral repertoires, the EPME-related pattern should be viewed as a descriptive comparison among extant lineages. Further validation will require broader taxon sampling, ancestral reconstruction, and experimental testing of receptor tuning breadth.”

      In summary, despite the large number of analyses performed, the authors do not really succeed in achieving the stated objective of reconstructing the evolutionary history of insect ORs, and the results obtained do not strongly support all the conclusions regarding the links between OR repertoires and environment or lifestyle.

      Thank you for this important comment. We agree that ‘reconstructing the evolutionary history of insect ORs’ was too broad a formulation and exceeded what our methods could reliably address. A rigorous reconstruction of evolutionary history requires well-resolved phylogenetic relationships across taxa, ancestral-state inference, and systematic analysis of gene duplication and loss events. We have therefore redefined the scientific objectives of this study. The revised manuscript no longer claims to have reconstructed the evolutionary history of insect ORs. Instead, it aims to systematically characterize insect OR diversity and classification patterns at the levels of sequence, structure, and docking profile, compare the correspondence among these levels, and describe differences in their distribution across insect taxa and broad macroevolutionary contexts.

      We have also reassessed the strength of different types of evidence concerning the relationships between OR repertoires and environment or lifestyle. For relationships between OR repertoire size and ecological traits, we repeated the genome-based benchmarking. After rerunning the ecological correlation sensitivity analyses using the updated OR count dataset, the main statistical results remained consistent with the original analyses. We therefore consider the current data to continue to support statistical associations between OR counts and ecological traits, with a degree of robustness. By contrast, we have removed results and discussion directly linking predicted OR function or recognition breadth to species ecology, and no longer use terms such as ‘docking-derived function’ or ‘functional community’ that might imply experimental evidence of function. The remaining macroevolutionary comparisons of docking profiles, such as differences in docking-profile composition among groups considered in relation to the EPME, are treated only as hypothesis-generating comparative patterns, rather than direct evidence of receptor functional evolution. We explicitly state that these hypotheses require future testing through functional experiments and more reliable ancestral-state reconstruction.

      In summary, we have redefined the study objectives and no longer describe this work as a reconstruction of insect OR evolutionary history. We have retained the statistical associations between OR counts and ecological traits that remained supported after reassessment, while removing results and discussion linking receptor function to ecology on the basis of molecular docking. We believe these revisions better align the scientific objectives and scope of the conclusions with the evidence provided by the available data and methods.

      We have revised the final paragraph of the Introduction to clarify the scope and objectives of this study (lines 80-91): “In this study, we characterized large-scale patterns of OR diversification across 115 species spanning 16 insect orders. Using a framework that integrates network analysis, protein structure prediction, and molecular docking, we constructed sequence-similarity (SSN), structural-similarity (StSN), and docking-profile similarity networks (dPSN) to classify ORs from three distinct perspectives. We identified differences in OR sequence-community diversity between holometabolous and non-holometabolous insects, as well as differences in docking-profile community composition among extant lineages whose order-level origins fall on opposite sides of the end-Permian mass extinction (EPME). Across this class-wide sampling, OR repertoire size was associated with adult diet, larval diet, and habitat, but not with circadian rhythm. Moreover, the conserved β-sheet in the extracellular loop 2 (EL2) region of Orco, together with its specialized binding-pocket architecture, is associated with its reduced ligand-binding capacity and represents a prominent structural specialization of the co-receptor lineage.”

      Reviewer #2 (Public review):

      The remarkable evolvability of the olfactory system enables animals to rapidly adapt to dynamic and chemically complex environments. Over the past two decades, substantial effort has been devoted to uncovering the evolutionary principles that drive the diversification of odorant receptors (ORs), yielding key insights into the forces shaping their striking variability in both vertebrates and insects. In this manuscript, Zhang and colleagues analyze the OR repertoires of over 100 insect species, leveraging sequence and structural similarity to infer patterns of gene family evolution within this diverse and ecologically important clade. By integrating sequence-based and structure-based comparisons, their study builds on a compelling and recently emerging line of research made possible by the advent of AlphaFold, which has previously clarified the phylogenetic relationship between insect Ors and the gustatory receptor gene family and revealed the unexpectedly deep evolutionary origins of this ancient structural fold.

      Applying this approach to a large set of ORs derived from species throughout the insect phylogeny, the authors confirm many previously reported patterns of OR evolution. More importantly, with their large-scale approach the authors found new structural features of different Or clades, resulting in intriguing testable hypotheses about Or evolution that have the potential to support future directions in studying this important gene family. For example, the authors identify a structural feature mostly unique to the OR co-receptor ORco, a beta-sheet in EL2, which they functionally show reduces odorant binding affinity - a key aspect of ORco, which does not bind ligands in the ancestral ligand-biding site. This is a particularly strong part of the manuscript, since the authors support their in silico-derived hypothesis with functional data.

      We thank the reviewer for the positive assessment of this part of our work.

      In an attempt to assess the relationship between sequence identity and structure on one hand and function on the other, the authors perform an in-silico structure prediction and chemical docking analysis. While not suitable to assess the actual function of individual receptors without functional testing, this strategy revealed potential receptor clades with differences in the number and type of docked chemicals. As a response to the previous reviews, the authors now changed their writing to refer to these results as 'docking-derived function' instead of just 'function'. While I appreciate that the authors changed their wording, this semantic change still can be read as referring to dockingderived predictions as conclusions of actual biological function. However, this is not the case. Any functional claims based solely on the docking approach need to be interpreted with caution, as in silico-only approaches are currently not well enough predictors for actual receptor function. Accordingly, I strongly recommend to remove the parts that connect receptor function to structure or ecology.

      Thank you for highlighting this important issue again. We agree with your comments and acknowledge that the presentation of molecular docking results in the previous revision still overstated their biological implications. Following your suggestion, we have removed all results and discussion that directly linked receptor function to species ecology solely on the basis of molecular docking, to avoid interpreting computational predictions as actual biological functions. We then systematically reviewed and revised docking-related terminology throughout the manuscript. For results derived entirely from docking analyses, we no longer use ‘docking-derived function’, ‘functional community’, or other expressions that might imply experimental evidence of function. Instead, we consistently use ‘docking profile’ to clarify that these analyses reflect similarities in computational docking patterns among receptors, rather than experimentally validated functional categories.

      Through these revisions, we hope to distinguish more clearly between computational docking profiles and experimentally validated receptor function, and to avoid biological interpretations of docking results that exceed the available evidence. We believe these changes bring the manuscript's conclusions into closer alignment with the strength of the evidence.

      Assessing their docking analyses with respect to species ecology, the authors identify correlations suggesting that Or repertoires might follow predictable evolutionary trajectories broadly aligned with species ecology. These are interesting hypotheses. However, due to the inability to predict actual receptor function the author's claims about ecology-function relationships are not well supported. Accordingly, the authors should change the respective parts of the manuscript in way that it is clear these are currently untested hypotheses. This includes adjusting the title, since the work does not find any experimental support for ecological tuning.

      Thank you for this important suggestion. We agree that the current molecular docking analyses cannot support direct conclusions about relationships between actual receptor function and species ecology. Following your suggestion, we have removed the results and discussion directly linking docking-based predictions of OR function to species ecology. For the remaining macroevolutionary comparisons, such as differences in docking-profile composition among groups considered in relation to the EPME, we explicitly state that these results represent only computational patterns observed in extant taxa. The evolutionary interpretations proposed from these patterns remain experimentally untested hypotheses and cannot be considered direct evidence of changes in actual receptor function.

      In addition, following your suggestion, we have changed the manuscript title to ‘Classwide sequence, structural, and docking-profile diversity of insect odorant receptors and structural specialization of Orco’. The new title no longer emphasizes ‘ecological tuning’ or ‘olfactory perception’, but instead focuses on class-wide diversification of insect OR sequences, structures, and docking profiles, together with the experimentally supported structural specialization of Orco.

      The following revision has been added in Result section (lines 367-369): “These observations should be interpreted as a docking-derived comparative pattern among extant lineages rather than direct evidence of ancestral functional changes across the EPME.”

      And Discussion section (lines 410-414): “However, this interpretation remains hypothetical. Because rapid OR evolution and limited cross-order orthology preclude reliable reconstruction of deep ancestral repertoires, the EPME-related pattern should be viewed as a descriptive comparison among extant lineages. Further validation will require broader taxon sampling, ancestral reconstruction, and experimental testing of receptor tuning breadth.”

      I believe that future studies combining functional testing with an explicit evo-eco framework could benefit from this work and the explicit hypotheses it generates. But the authors should be careful to not overstate their in-silico derived results.

      Thank you for this suggestion. We have revised the manuscript accordingly.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Below are my comments in response to the rebuttal letter. I thank the authors for their careful revision and detailed responses to the points raised in my review.

      (1) OR annotation pipeline and correlation analyses

      Previous comment: "One can note aberrant numbers of ORs for certain species (much lower than reality), which indicates that the pipeline probably did not function correctly for all genomes. In the absence of a validation step comparing the results with already known OR repertoires, it is difficult to estimate the overall quality of the data. [...] It calls into question certain results that rely on this total number, such as the correlation between the number of ORs and genome size, lifestyle, and diet."

      The response made by the authors on that point is not particularly convincing. They compared their OR gene annotations with previous reports in the same species, but this comparison is irrelevant because the chosen reference OR repertoires are actually a mix of transcriptome and genome annotations (the OR counts for Harmonia axyridis, Plutella xylostella, Pierus brassicae, Dendrolimus kikuchii, Carposina sasakii, Hermetia illucens, Chlorops oryzae and Propsilocerus akamusi are from transcriptomes). As not all OR genes are expressed in a given tissue, the number of ORs in transcriptomes is always lower than in genomes. Furthermore, comparisons with entire OR gene repertoires essentially demonstrate that their annotation was of good quality for species in which OR sequences were already present in the query OR database (Bombyx mori, Drosophila melanogaster, Anopheles gambiae) but was of bad quality for species in which OR sequences were NOT in the query (Diabrotica virgifera). However, re-annotation of Diabrotica led to an OR repertoire of 78 complete genes, vs. 124 in the manually curated genome annotation (1/3rd of the genes are missing). So these supplementary analyses made by the authors are not particularly in favor of an overall good quality of OR gene repertoires in the >110 species studied. They have also repeated the correlation analyses using 'full' OR gene sets where available. This showed that the correlation of OR count with genome size is no longer valid, but the correlation with diet or lifestyle is. However, if a mix of gene numbers from transcriptomes and genomes was used (see my comment above), I am afraid that these novel correlation analyses are not relevant. In conclusion, I would disagree with the claim that "These new benchmark and sensitivity analyses directly strengthen the evidence supporting our dataset and its ecological interpretations".

      Thank you for further highlighting this issue. As detailed in our response to the Public Review, we repeated the OR annotation benchmarking, excluded transcriptome-only repertoires, and restricted comparisons to genome-based reference datasets or genome-based datasets supported by transcriptomic evidence. The updated results show that, for most species with comparable reference data, the number of ORs identified in this study exceeds 70% of the previously reported number. We also explicitly acknowledge that repertoire size may still be underestimated in some species.

      We also directly tested whether query database composition systematically affected the number of annotated ORs. No significant difference was detected between species with and without conspecific OR queries (P = 0.841; P = 0.413 after excluding Anagrus nilaparvatae). Thus, we currently find no evidence that conspecific queries systematically increase the final OR count.

      In addition, we repeated the sensitivity analyses of ecological correlations using the updated OR count dataset. The main statistical results remained unchanged: OR counts were still significantly associated with adult diet, larval diet, and habitat, but not with circadian rhythm. We therefore consider these statistical associations between OR counts and ecological traits to be reasonably robust to the annotation differences currently detectable. Detailed results are provided in our response to the Public Review and in the revised supplementary text S1.

      (2) sequence similarity-based clusters

      Previous comment: "The authors attempted to categorize ORs in several ways, starting with the reconstruction of sequence similarity networks. The approach is interesting, but in the end, the results do not seem to be sufficiently exploited. [...] For example, clusters SeqC26 or SeqC28, which appear to be shared by many insects, are potentially very interesting. It might have been relevant to combine this similarity-based clustering approach with phylogenetic reconstructions within each shared cluster."

      The authors have followed this suggestion and identified an orthogroup conserved across several insect orders, which is an interesting observation. However the phylogeny of this orthogroup should be provided in supplementary data, which does not seem to be the case.

      We thank the reviewer for the positive assessment. The corresponding phylogenetic tree has been added to the supplementary data S6.

      (3) structure-based clusters

      Previous comment: "The clusters shared by several orders are not truly exploited, which does not provide new insight into the evolution of ORs"

      The authors show an interesting feature for a cluster of ORs from several insect orders, with a large putative binding pocket. However, it is not clear whether they verified or not if this cluster also correspond to an orthogroup or if this shared feature (a large binding pocket) is a convergence. They also improved the discussion of their results on a cluster of fly ORs with a long intracellular domain, which is a good thing.

      Thank you for this important question. To determine whether StrC23, a structural community shared across multiple insect orders, corresponds to a conserved orthogroup, we performed additional phylogenetic analyses of its OR sequences and identified orthology relationships within the community based on the gene tree. The results showed that StrC23 does not correspond to a single orthogroup spanning insect orders, but instead contains 156 distinct orthologous groups. Of these, 24 encompass more than 10 species. Among these 24 larger groups, one contains ORs from Hymenoptera, Diptera, and Trichoptera, and two contain ORs from both Lepidoptera and Trichoptera. The remaining groups show pronounced order specificity, including nine Hymenoptera-specific groups, eight Lepidoptera-specific groups, and four Diptera-specific groups.

      These results indicate that the large putative binding pocket characteristic of StrC23 is not explained by a single, broadly conserved orthogroup spanning multiple orders, but is distributed across several phylogenetically independent OR lineages. We therefore consider that this shared structural feature cannot simply be interpreted as direct retention within a conserved orthologous lineage. It may reflect convergent evolution toward similar structural features in different OR lineages, or the retention of an older structural feature in multiple OR lineages that subsequently underwent substantial sequence divergence. The current data do not allow us to distinguish conclusively between these two possibilities. We have added the phylogenetic analysis of StrC23 to the revised manuscript to clarify the evolutionary relationships within this structural community.

      The following revision has been added (lines 208-218): “To further investigate the evolutionary basis of this broadly shared structural feature, we examined the phylogenetic and orthology relationships among StrC23 receptors. StrC23 was divided into 156 orthologous groups, 24 of which contained ORs from more than 10 species. However, only a few of these larger groups spanned multiple insect orders: one included ORs from Hymenoptera, Diptera, and Trichoptera, while two included ORs from both Lepidoptera and Trichoptera (Supplementary Data S6). Thus, StrC23 does not represent a single conserved cross-order orthologous lineage, but instead comprises multiple phylogenetically distinct OR lineages. Its shared large-pocket architecture therefore cannot be explained solely by conservation of a single orthologous lineage and may reflect either structural convergence or retention of an ancient structural feature followed by substantial sequence divergence.”

      (4) molecular dynamics simulation

      Previous comment: "The molecular dynamics modelling does not add anything in the way it is conducted (5 ns is too short)."

      The authors argue that such a short simulation is sufficient for testing the interaction between a compound and the extracellular beta-sheet of the coreceptor Orco. I'm not a specialist enough to confirm whether this is actually true, but I trust the author's expertise in this area.

      We thank the reviewer for the positive assessment.

      (5 / 6) molecular docking and functional extrapolation

      Previous comment: "in the absence of functional validation, this 20% true positive rate is not sufficient to extrapolate OR function"

      The authors argue that molecular docking was not used to identify specific OR/ligand pairs, but rather to "estimate the potential binding tendencies of OR repertoires". I agree with this point; correlations were made using "binding breadth index values" that are calculated for entire OR repertoires. The text of the manuscript has been edited to refer to "docking-derived" functional clusters and binding breadth index, and to emphasise that the results should be interpreted with caution, which is a good point as it will prevent potential misinterpretation by the reader. However, this does not change the fact that interpreting the olfactory capacity of one species based on docking-derived binding breadth values of its OR repertoire is highly speculative (especially when OR repertoires are incomplete) and the chance that results of this part of the work will enlighten the evolution of OR on a broad scale is low.

      Thank you for raising this concern. We agree that repertoire-level analyses do not resolve the inherent limitations of molecular docking in predicting actual receptor function. Although such analyses summarize overall docking patterns rather than individual OR–ligand interactions, they still cannot reliably define a species’ true olfactory capabilities. It therefore remains insufficient to describe a species' actual olfactory capabilities solely on the basis of docking-derived binding breadth. As detailed in our response to the Public Review, we have addressed this issue by following your suggestions and those of Reviewer 2 and removing the results and discussion that used the binding breadth index to directly link predicted receptor function to ecological traits such as diet, habitat, and circadian rhythm. We have also further standardized the terminology, replacing ‘docking-derived function’, ‘functional community’, and other expressions that could imply experimentally established function with ‘docking profile’, to emphasize that these results describe computational docking patterns rather than experimentally determined receptor functions. Through these revisions, we hope to distinguish more clearly between computational docking patterns and experimentally established receptor functions, and to restrict the interpretation of the related macroevolutionary comparisons to what the current data can support.

      (7) EPME

      Previous comment: "ORs of non-holometabolous insects cannot be considered equivalent to those of species that existed before the Permian-Triassic extinction"

      The authors have now clarified the fact that they compared extant insect orders rather than ancestral and derived OR repertoires.

      We thank the reviewer for the positive assessment of the revised manuscript.

      (8 to 16)

      The authors have carefully considered all the points raised in the recommendations for the authors, improving the manuscript on several aspects.

      We thank the reviewer for the positive assessment of the revised manuscript.

      Minor comment

      Line 217: Orco did not emerge in the ancestor of Zygentoma, it emerged in the common ancestor of Zygentoma and Pterygota.

      Thank you for pointing this out. We have corrected the statement.

      Reviewer #2 (Recommendations for the authors):

      The authors have done a good job in their revision of the manuscript. They addressed many of the reviewers' concerns well.

      We thank the reviewer for the positive assessment of our previous revision.

      However, the functional claims are still too strong and need to be removed or tempered down as described in the public review.

      Thank you for this suggestion. We have removed or tempered the functional predictions in the manuscript in accordance with the Public Review. Please see our response to the Public Review for details.

      Further, the manuscript title is not a good fit for the content. I would argue that the paper does not 'reveal ecological tuning shaping olfactory perception', since Or structurefunction relationships are involved in olfactory sensation or detection. Perception, however, requires interpretation, which is done by neural circuits but not receptors expressed within. Further, I still think that referring to 'ecological tuning' is far-fetched based on the docking approach that cannot really resolve the tuning of receptor sets. I would think that the work reveals interesting correlations between docking-derived population tuning and ecology. But that does not necessarily have anything to do with actual function and thus may not make much sense to advertise in the title or abstract.

      Thank you for your suggestions concerning the manuscript title. Following this comment, we have changed the title from ‘Evolutionary dynamics of insect odorant receptors reveal ecological tuning shaping olfactory perception’ to ‘Class-wide sequence, structural, and docking-profile diversity of insect odorant receptors and structural specialization of Orco’ The new title no longer emphasizes ecological tuning or olfactory perception, but instead focuses on the two aspects of this study supported by the most direct evidence: class-wide patterns of sequence and structural diversification in insect ORs, and the experimentally supported structural specialization of Orco.

      Consistent with the revised title, we have further adjusted the relevant wording in the Abstract, Results, and Discussion, and removed content directly linking receptor function to species ecology on the basis of docking results. We believe that the revised title and abstract more accurately reflect the main content of the study and the scope of its evidence, while avoiding functional or ecological interpretations of computational predictions that exceed their evidential support.

      I suggest selecting a title that fits the sequence/structure/in silico docking nature of their large macroevolutionary approach and findings better. After all, apart from the orco work this manuscript is mostly a resource of in silico predictions.

      Thank you for this suggestion. We have revised the manuscript title.

      Author response table 1.

      Numbers of OR genes in previously annotated species.

      Reference

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      Engsontia P, Sangket U, Chotigeat W, Satasook C. 2014. Molecular evolution of the odorant and gustatory receptor genes in lepidopteran insects: implications for their adaptation and speciation. J Mol Evol 79: 21-39.

      Ma Y, Guo Z, Wang L, Wang B, Huang T, Tang B, Zhang G, Zhou Q. 2022. The genome of the rice planthopper egg parasitoid wasp Anagrus nilaparvatae casts light on the chemo- and mechanosensation in parasitism. BMC Genomics 23: 541.

      Mier P, Fontaine JF, Stoldt M, Libbrecht R, Martelli C, Foitzik S, Andrade-Navarro MA. 2022. Annotation and Analysis of 3902 Odorant Receptor Protein Sequences from 21 Insect Species Provide Insights into the Evolution of Odorant Receptor Gene Families in Solitary and Social Insects. Genes (Basel) 13.

      Paddock KJ, Corcoran JA. 2025. Life-stage dependent behavior mimics chemosensory repertoire diversity in a belowground, specialist herbivore. G3 (Bethesda) 15.

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      Tanaka K, Uda Y, Ono Y, Nakagawa T, Suwa M, Yamaoka R, Touhara K. 2009. Highly selective tuning of a silkworm olfactory receptor to a key mulberry leaf volatile. Curr Biol 19: 881-890.

      Wang Q, Dicke M, Haverkamp A. 2023. Sympatric Pieris butterfly species exhibit a high conservation of chemoreceptors. Front Cell Neurosci 17: 1155405.

      Xiao S, Ye X, Wang S, Yang Y, Fang Q, Wang F, Ye G. 2023. Genome assembly of the ectoparasitoid wasp Theocolax elegans. Sci Data 10: 159.

    1. eLife Assessment

      This paper presents important findings on how the shapes of leaves might be biased towards simpler shapes due to biases in how variation is generated by developmental processes rather than selection. The authors present convincing evidence that combines image analysis of a herbarium dataset and computational analysis of a model of leaf development. The paper should be of interest to diverse researchers, ranging from plant development to the evolution of complexity more broadly.

    2. Reviewer #1 (Public review):

      [Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have addressed the comments raised in the previous round of review.]

      Summary:

      The authors aim to understand, in the context of leaf shape, how the constraints imposed by development inform evolution. Leaf shape is a good place to study the influence of development on evolution because it is a trait that exhibits a lot of diversity, and the developmental mechanisms that give rise to leaf shapes are apparently rather conserved across angiosperms.

      As part of the motivation for their work, the authors cite a previous study (Geeta et al), which found that in angiosperm phylogenies, transitions from complex to simple leaf shapes occur through evolution more often than transitions in the opposite direction. Is this due to developmental constraints or adaptation?

      The authors undertake two parallel lines of work:

      (1) Extending the study of Geeta et al with more data, consisting of both phylogenies and a shape classification dataset. The conclusion from this line of inquiry is that transitions from lobed to unlobed leaves are more common than transitions away from unlobed leaves.

      (2) The authors conduct evolution simulations in a computational model of leaf development. Here, they look at {\it neutral} mutations and whether simply neutral evolution is sufficient to drive the observed trend.

      The conclusion of the second part of the work is that the driver of the evolution toward simple leaf shape is entropy: there are more ways to make unlobed leaves than to make lobed leaves (at least in terms of gene regulation parameters that will produce the two leaf types). The argument is that random gene regulatory networks are more likely to produce unlobed leaves than lobed leaves; therefore, neutral evolution drives this trend.

      Data Analysis:

      Roughly $9000$ images of leaves were classified into 4 categories: unlobed, lobed, dissected, and compound. These labels were applied to the tips of 5 phylogenetic trees of angiosperms (3 resolved at the genus level and 2 at the species level). By fitting a continuous-time Markov chain to the labelled trees, the authors claim that there is a significantly higher rate of transition to the unlobed leaf shape compared to transitions to more complex shapes.

      Simulation:

      First, the authors validate a computational model (Runions et al) for leaf growth on an experimental dataset. By changing parameters in the model, they can recapitulate the morphological changes in the shapes of Arabidopsis leaves engendered by expression of two particular genes.

      Then the authors run an evolutionary model (without selection, just random mutations) on top of the computational leaf development model. As the random walk in parameter space reaches a stationary distribution, they look at both the proportions of the leaf categories in the steady state as well as the transition rates between different categories. The result is that transitions to unlobed leaves are more common than from unlobed leaves.

    3. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The authors aim to understand, in the context of leaf shape, how the constraints imposed by development inform evolution. Leaf shape is a good place to study the influence of development on evolution because it is a trait that exhibits a lot of diversity, and the developmental mechanisms that give rise to leaf shapes are apparently rather conserved across angiosperms.

      As part of the motivation for their work, the authors cite a previous study (Geeta et al), which found that in angiosperm phylogenies, transitions from complex to simple leaf shapes occur through evolution more often than transitions in the opposite direction. Is this due to developmental constraints or adaptation?

      The authors undertake two parallel lines of work:

      (1) Extending the study of Geeta et al with more data, consisting of both phylogenies and a shape classification dataset. The conclusion from this line of inquiry is that transitions from lobed to unlobed leaves are more common than transitions away from unlobed leaves.

      (2) The authors conduct evolution simulations in a computational model of leaf development. Here, they look at {\it neutral} mutations and whether simply neutral evolution is sufficient to drive the observed trend.

      The conclusion of the second part of the work is that the driver of the evolution toward simple leaf shape is entropy: there are more ways to make unlobed leaves than to make lobed leaves (at least in terms of gene regulation parameters that will produce the two leaf types). The argument is that random gene regulatory networks are more likely to produce unlobed leaves than lobed leaves; therefore, neutral evolution drives this trend.

      Data Analysis

      Roughly $9000$ images of leaves were classified into 4 categories: unlobed, lobed, dissected, and compound. These labels were applied to the tips of 5 phylogenetic trees of angiosperms (3 resolved at the genus level and 2 at the species level). By fitting a continuous-time Markov chain to the labelled trees, the authors claim that there is a significantly higher rate of transition to the unlobed leaf shape compared to transitions to more complex shapes.

      Simulation

      First, the authors validate a computational model (Runions et al) for leaf growth on an experimental dataset. By changing parameters in the model, they can recapitulate the morphological changes in the shapes of Arabidopsis leaves engendered by expression of two particular genes.

      Then the authors run an evolutionary model (without selection, just random mutations) on top of the computational leaf development model. As the random walk in parameter space reaches a stationary distribution, they look at both the proportions of the leaf categories in the steady state as well as the transition rates between different categories. The result is that transitions to unlobed leaves are more common than from unlobed leaves.

      We thank the reviewer for the helpful and clear summary of our work.

      General Comments

      The authors use angiosperm phylogenies from other works as the basis for the data analysis part of their work. Given the centrality of these phylogenies for their conclusions, more information is needed about how these phylogenies were constructed and what they mean. What is the timescale that they span? What method is used to infer them? What regions of DNA were sequenced in order to build the phylogenies? Also, maybe some more discussion of angiosperm evolution (e.g., when was the most recent common ancestor of all angiosperms?) would help put the study in context.

      We also need a more in-depth discussion of the computational model. What are all the $>100$ parameters doing, and what informs the seemingly strange mutational model that changes parameters by 3 orders of magnitude?

      I am confused about how the rates of transitions were inferred from the phylogeny. Here, one has a phylogeny inferred by some method (which needs to be described in more detail), and just the leaves are labelled. It is stated in the methods that BayesTraits was used to infer the transition rates. I realize this method is probably documented elsewhere, but a bit of a summary of how it works and how to interpret its results would (1) make the paper more selfcontained and (2) if the algorithm is credible, make the results firmer.

      We thank the referee for the suggestion to make the paper more accessible. The tool we use to infer transition rates from the phylogenies, BayesTraits, is standard in the field. However, the referee is right that for an interdisciplinary journal, it may be helpful to more fully flesh out how these methods work. To that end, we have added an additional section S2.A "Phylogenetic rate inference" in the supplementary information that includes a longer description of how BayesTraits works, and how we used it to infer transition rates from phylogenies.

      All trees are shown in the supplementary information section S2.B "Phylogenetic trees" with scale bars showing the amount of time or genetic change that the trees span. We have added details of the specific genes used to construct the trees in our analysis in Methods section F. For a broader discussion of angiosperm evolution, there is supplementary information section S4 "A short literature review of the adaptive significance of leaf shape".

      Regarding the more in-depth discussion of the computational model, we have added supplementary information section S1.A "Leaf model details" to give a more detailed description of the leaf model. We chose our mutational scheme over other methods, such as uniform or Latin hypercube sampling, as the biologically relevant ranges for each parameter were unknown. Mutations of multiple orders of magnitude are a way to sample both parameters with large and small ranges with the same scheme.

      Additionally, larger mutations help to move a walk out of a local region if it is stuck while small mutations ensure sensitive parameters are still varied. We use multiple schemes for additional robustness.

      I am a bit skeptical of the authors' interpretation of the biological trend (of complex to simple leaf shapes) as being driven by neutral evolution. Why does one expect that the mutations generated by the random walk models described in the work are in fact neutral mutations?

      A random walk is a well-established way of modelling the dynamics of neutral evolution in the monomorphic regime, where the population has a narrow diversity of different genotypes. In the higher mutation rate polymorphic regime, where the diversity of genotypes in the population is larger, we also expect that a random walk should still recapitulate the correct average transition rates. The purpose of the simulations is not to model every aspect of population genetics, but to ask whether developmental bias alone is sufficient to generate the observed directional asymmetry. By assigning equal fitness to all viable leaves, we isolate the contribution of development from that of selection. The agreement with the phylogenetic transition rates therefore demonstrates sufficiency rather than exclusivity: selection may also contribute, but it is not required to explain the observed bias. We discuss the evidence for the role of adaptation in leaf shape further in supplementary information section S4 "A short literature review of the adaptive significance of leaf shape".

      - If the entropy of simple leaf shapes is higher than that of complex leaf shapes, why did we have complex leaves at all? I suspect the authors might argue that this is due to selection. In that case, what allows these complex shapes to become simpler? Wouldn't they be losing the selective advantage that drove them to be more complex in the first place? Or maybe the idea is that the rates are inferred assuming some steady state that generates the phylogeny? I did not understand this point.

      The entropy language is a useful framing. Within that framework, one can view our study as showing that the entropy (defined here as the logarithm of the volume of parameter space mapping to a phenotype) of simple leaf shapes is higher than that of complex leaf shapes. If this entropy were to be ignored, and were we not to take fitness differences into account, then all states would be equally likely in our simulations. What we show is that the differences in entropy, which are related to differences in volumes of the parameter space that map to different phenotypes, also affect the rates. The inferred transition rates for both simulation and phylogeny from unlobed to more complex shapes are lower than the reverse but not zero. Therefore, complex leaf shapes arise stochastically through mutation and in this model would eventually reach a steady-state proportion, even in the absence of selection. This steady state proportion for complex leaves would be smaller than that of unlobed leaves, but non-zero.

      Are the rates of transitions between leaf types inferred for the phylogeny assuming that the phylogeny is generated by the steady state of some Markov process? (I think the answer is no: in that case, how does one explain the initial condition?)

      The tool we use to infer transition rates from phylogenies—BayesTraits—allows the initial state at the root of the tree to vary during the numerical optimisation (Pagel, 1994). Therefore, it is not assumed that the initial state is generated by the steady state of the Markov process.

      If I take the mutation model (random walk) seriously, then shouldn't I expect that this steady state obeys detailed balance? In that case I should have $p_i r_{i\to j} = p_j r_{j\to i}$ for each of the occupancies $\{ p_i\}$ and transition rates $r_{i\to j}$ for the shape categories. How close are the rates inferred from the phylogenies to obeying detailed balance? Presumably, the Markov chain fitted to the simulation data obeys detailed balance because the mutation model itself does?

      BayesTraits allows off-diagonal transition rates of the rate matrix to vary freely during numerical optimisation (Pagel, 1994). Therefore, there is no requirement for detailed balance to hold for the inferred rate matrix. For our simulations, the mutations are symmetric at the parameter level, therefore at this level, the process would be expected to obey detailed balance. We investigated this further by calculating the posterior signed equilibrium flux difference $\pi_iq_{ij}-\pi_jq_{ji}$ for all inferred rate matrices (see new material in supplementary information Fig S23). The posterior signed equilibrium fluxes for each transition pair in the simulations have 95% credible intervals overlapping with 0, which is consistent with detailed balance. Some phylogenetic state pairs are inconsistent with detailed balance. This could be due to something other than symmetric neutral drift acting on these specific transitions, such as selection or mutation biases not captured by the model. However, the identity of the transition pairs that are inconsistent with detailed balance varies between phylogenies, indicating that these apparent violations could be noise due to phylogenetic uncertainty. In any case, a violation of detailed balance does not disprove a developmental bias explanation, it just suggests that the underlying process is more complex than the reversible evolutionary algorithm we use in our leaf-model simulations. However, introducing non-reversible mutation dynamics into our leaf-model simulations would require additional assumptions and parameters describing mutational asymmetries, which are not necessary to reproduce the key bias towards unlobed.

      I find it hard to take the discussion of development seriously without some consideration of mechanics. Presumably, the mechanics are hidden in the computational leaf development model, but this model is not discussed in enough detail for the reader to know. It seems to me that the interesting question is: what are the {\it mechanical} constraints on development that drive the apparent trend in evolution towards simpler leaf shapes? Maybe it is something about the type of differential growth needed to make complex leaf shapes less robust to mutation. But in this case, I would assume that selection plays a role in the complexity of shape. In any case, a better understanding (or explanation) of the computational model is needed to make this interpretation.

      We thank the referee for the suggestion to make the paper more accessible. We have added a new, more detailed and pedagogical description of the model from (Runions, Tsiantis and Prusinkiewicz, 2017) in the supplementary information section S1.A “Leaf model details". We also note that Fig. 5 in the methods gives an overview of how the model works, including some mechanical aspects of development and growth.

      More generally, mechanics is one component of the developmental map that determines which parameter combinations produce viable leaf morphologies. Our analysis concerns the geometry of this complete developmental map, irrespective of whether its constraints arise from gene regulation, tissue mechanics, or their interaction.

      On the interesting question of what is causal, perhaps the example in figure 2 is helpful. We focus on two parameters, a morphogen repression strength, and a duration of growth. A key physical process here is called webbing, where cellular growth fills in the gaps between branching veins. This process flattens the leaf structure and creates a continuous, solid leaf blade (lamina). Strong webbing, characterized by a significant resistance to stretching and bending, results in a smoother margin (Runions, Tsiantis and Prusinkiewicz, 2017). The morphogen repression strength affects the physical parameters that determine how strong the webbing is. The duration of growth determines how long the leaf has to grow. If an unlobed leaf has some marginal patterning that results in small protrusions, such as serrations, increasing the duration of growth will amplify these protrusions, eventually forming lobes. Similarly, increasing the strength of morphogens that repress growth along the margin will deepen the sinuses between protrusions, resulting in lobes. Combinations can result in highly dissected and compound leaves, as illustrated in figure 2. Conversely, decreasing either or both will result in a smoother margin and eventually leaves with an unlobed shape. Thus, varying these two developmental parameters varies the physical processes that determine leaf shape. The mechanics of growth operate downstream of these parameters that we vary in our evolutionary simulations according to the details of the leaf developmental model.

      Some discussion of timescales is needed, especially when invoking neutral evolutionary arguments. If a neutral mutation occurs, its time to fix in a population of size $N$ is $\sim N$ generations. What are the relevant angiosperm population sizes and the number of mutations that separate branches on the tree? Are timescales remotely consistent with e.g., the age of angiosperms on Earth?

      Neutral processes have a well-established role in key aspects of angiosperm evolution, for example genome complexity (Lynch and Conery, 2003). This would suggest that the relevant timescales and generation times are not completely prohibitive of neutral processes also playing a role in the evolution of angiosperm leaf shape. Effective population sizes in plants are highly variable but estimates span 10^3-10^6. Assuming diploidy (and therefore average fixation time of 4Ne) and generation times of 1-10 years, this gives fixation timescales of 10^3-10^7 years. This is within the timescales of the trees we analyse, which span >150 million years.

      Reviewer #2 (Public review):

      Strengths:

      The paper's underlying question is interesting, extending the authors' prior work on RNA along similar conceptual lines. The paper combines both image analysis of leaves and a computational analysis of a simple model of leaf development.

      Weaknesses:

      The entire paper is based on the Runion model. More intuition about the Runion model would be useful for a broader readership that cares about the evolutionary aspect of this, but may not know the developmental model in question. Obviously, this is prior well-established work, but 2 - 3 sentences highlighting the key structural aspects of such a model would be great.

      Currently, that intuition is found implicitly in a sentence on page 2 ("complex leaf shapes need more specificity in their GRNs than their simpler unlobed leaf shape"), but the reader is left wondering - is the Runion model a detailed mechanistic one with multiple interacting genes/proteins? If so, how many? Or is it just 2 - 3 genes but with complexity entirely in how long they are each expressed/when they are turned off, etc.

      We thank the referee for the suggestion to make the paper more useful for a broader readership. To that end, we have added a more detailed description of the (Runions, Tsiantis and Prusinkiewicz, 2017) model in supplementary information section S1.A "Leaf model details".

      The Runions model has nearly 100 free parameters. Random walks in 100dimensional spaces have generic properties like a tendency to move toward regions of larger volume that have nothing to do with leaf biology. How do you disentangle the geometry of high-dimensional random walks from genuinely biological developmental bias? Would a toy model with 100 parameters and arbitrary phenotype categories also show "bias toward simplicity" if "simple" phenotypes occupy more volume?

      Our argument is largely independent of the number of parameters. While it is true that most of the volume is near the surface in a high-dimensional space, our argument is about the relative volumes of the sets of parameters that map to each of the four phenotypes, an entropic argument if you wish. The basic intuition is that a simple phenotype needs fewer parameters to be fine-tuned, and so a larger volume of parameter space will map to a simpler phenotype.

      The question about a toy model with arbitrary phenotypes is helpful because it allows us to clarify that what we are illustrating here with the biologically realistic example of leaf shapes is a much more generic principle. We can say with confidence that if the toy model generates a many-to-one set of outputs (phenotypes) through an algorithmic process whose description length does not grow faster than logarithmically with the size of the genotype space, then it should produce a bias towards simplicity regardless of the number of dimensions, see for example Johnston et al. (2022) and Dingle, Camargo and Louis (2018) for a longer discussion of this more general point which is based on arguments from algorithmic information theory (AIT). We do not use that framing in the current paper because the basic intuition for GRNs, that more complex phenotypes need more parameters to be fine-tuned and so have relatively smaller volumes, is more straightforward to understand than the more abstract AIT arguments. Our general prediction that this principle should hold more widely for GRNs can be made both by the more formal AIT route, or via the more heuristic fine-tuned parameter route.

      It is also true that random walks can have biases towards regions of parameter space with high robustness (Van Nimwegen et al., 1999). For our model, this would mean a bias towards valid leaves, however we have no reason to expect this would bias the rates between different valid leaf shapes. We address this point in methods section C.

      The discussion of Figure 4 (PCA of parameter space) uses "area" loosely when what's actually being measured is bin count in a 2D projection of a highdimensional space. I would think that, in general, PCA projections can be misleading about volume in the full parameter space, but I can't tell if that's an issue in this case. Some comments/thoughts here would be useful.

      The quantitative estimate of phenotype frequencies is computed directly in the full parameter space and does not depend on PCA. We estimate that the total volume of viable leaves maps to simple unlobed leaves about 80% of the time. However, the volume is extremely high-dimensional, and so hard to visualise. PCA is used solely to provide an interpretable visualization of this otherwise high-dimensional structure. The PCA plots in Fig 4 and Fig S16 are there to be illustrative, not quantitative. Because the volume differences are large, we do not think that the projections of the main PCA components would be misleading on at least the ordering of the sizes of the parameter space components that map to each leaf shape. We provided a similar analysis for other projections -- PC1-PC6 (supplementary information section S3.B "PCA occupancy for higher dimensions"), finding the same trend. To make this point clearer, we have now changed the sentence in the Fig. 4 caption slightly “This (reveals that --> illustrates how) unlobed leaves occupy a larger region of model parameter space than more complex shapes and that this larger space also contains the majority of more complex leaves.”

      The classifier validation section is in the Methods section, but it seems critical to the whole story. The < 80% agreement with manual classification could propagate to the rest of the estimates in the paper. Again, some comments/thoughts here would be useful.

      The review raises a valid concern. In response, we have repeated the analysis of the agreement between by-eye and automatic morphometric classification and produced a confusion matrix for the two classification methods to make the uncertainty clearer (see supplementary information Fig S7, section S1.E). The confusion matrices show that the agreement between the by-eye and automatic morphometric methods is high for unlobed, dissected and compound. The main source of disagreement is leaves that were classified as lobed by eye being classified as either unlobed or dissected by the automatic morphometric method. The proportion of by-eye lobed leaves classified by the automatic-morphometric method as either unlobed (27%) or dissected (23%) is relatively balanced, which we think will cancel out some error as well. Moreover, we find that the agreement between the automatic-morphometric method and by-eye classification increases to 90.0% when using the categories unlobed and all other categories grouped into one (complex). This is the most important classification for our finding that development and phylogeny are both biased towards unlobed.

      The authors should explain Mut2 and Mut5 in the main paper with a sentence or two, at least schematically, because how you mutate is obviously very relevant to interpreting a paper about biases in variation.

      We agree that this information should be readily available in the main text. In response, we have added a sentence to the results section C for more detail on the random walk.

      "[We mutated the initial sample using a random walk algorithm with two different mutational schemes, MUT2 (alg. 1) and MUT5 (alg. S2).] These algorithms work by iterating through model parameters one by one and perturbing the value by a small amount. We then [automatically classified the resulting shapes...]"

      However, we note that in methods section C there is already a more detailed description of both algorithms.

      “MUT2 (alg. 1) iterates through the parameters in a random order, and attempts to change the parameter by a value selected at random from an array of numbers randomly generated at 3 different orders of magnitude. MUT5 (alg. S2) is the same as MUT2 except that each mutation is additionally scaled by 10% of the current parameter’s range within the initial leaves (fig. S6). The aim here was to provide some way of accounting for the biologically relevant sampling range."

      Moreover, the MUT2 algorithm is described in pseudocode in Algorithm 1 in the main text, and the pseudocode for MUT5 is in supplementary information section S1.C, as algorithm S2.

      The two mutational schemes use additive perturbations to individual parameters. Real mutations presumably affect regulatory networks in more structured ways (e.g., changing binding affinities that affect multiple parameters simultaneously). How sensitive are the results to the assumption of independent single-parameter mutations?

      The referee raises an interesting and well-known issue concerning this widely studied class of GRN models. Without a detailed understanding of how individual genetic mutations map onto model parameters, it is difficult to determine with confidence whether a mutation would produce correlated changes in certain sets of parameters. Our main argument, however, is that the primary source of the observed bias is geometric: the volume of parameter space (or equivalently, the entropy) corresponding to simple leaf morphologies is substantially larger than that corresponding to complex morphologies. As long as mutations explore parameter space approximately symmetrically, even if they involve correlated changes in multiple parameters, larger phenotype regions will tend to be encountered more frequently and retained for longer than smaller regions. We therefore expect the observed bias to be robust to many alternative mutation models, as is already supported by the agreement between our two mutational schemes, although quantifying this robustness is an interesting direction for future work.

      The connectedness argument is made using a 2D PCA projection. Is there a way to check this statement in the full parameter space or perhaps in higherdimensional projections to test the robustness of this result? Connected components can merge/split under different projections.

      We agree that the 2D projection alone is limited. We therefore performed a new analysis using the same method but in the full dimensional parameter space. The nearest neighbour graph for the full-dimensional data has the following numbers of connected components: unlobed-146, lobed-273, dissected-255, compound-315. This follows the same pattern identified for the PC1-PC2 projection, that unlobed splits into fewer connected components than other leaf shape categories. We have added this result to the caption in figure 9.

      References:

      Dingle, K., Camargo, C.Q. and Louis, A.A. (2018) ‘Input–output maps are strongly biased towards simple outputs’, Nature Communications, 9(1), p. 761. Available at: https://doi.org/10.1038/s41467-018-03101-6.

      Johnston, I.G. et al. (2022) ‘Symmetry and simplicity spontaneously emerge from the algorithmic nature of evolution’, Proceedings of the National Academy of Sciences, 119(11), p. e2113883119. Available at: https://doi.org/10.1073/pnas.2113883119.

      Lynch, M. and Conery, J.S. (2003) ‘The Origins of Genome Complexity’, Science, 302(5649), pp. 1401–1404. Available at: https://doi.org/10.1126/science.1089370.

      Pagel, M. (1994) ‘Detecting correlated evolution on phylogenies: a general method for the comparative analysis of discrete characters’, Proceedings of the Royal Society of London. Series B: Biological Sciences, 255(1342), pp. 37–45. Available at: https://doi.org/10.1098/rspb.1994.0006.

      Runions, A., Tsiantis, M. and Prusinkiewicz, P. (2017) ‘A common developmental program can produce diverse leaf shapes’, New Phytologist, 216(2), pp. 401–418. Available at: https://doi.org/10.1111/nph.14449.

      Van Nimwegen, E., Crutchfield, J.P. and Huynen, M. (1999) ‘Neutral evolution of mutational robustness’, Proceedings of the National Academy of Sciences, 96(17), pp. 9716–9720. Available at: https://doi.org/10.1073/pnas.96.17.9716.

    1. eLife Assessment

      This important study demonstrates how individual taste preferences change over time, how these changes are reflected in cortical activity, and how sensory experience contributes to reshaping both. The evidence is convincing and broadly supports the main conclusions. The findings should be of interest to neuroscientists studying sensory processing and cortical plasticity.

    2. Reviewer #1 (Public review):

      [Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have addressed the comments raised in the previous round of review.]

      Summary:

      Maigler et al. set out to test the hypothesis that individual differences in taste preferences are (in part) due to individual differences in central taste processing. They first tested rats' preferences for a variety of taste stimuli on multiple days. They then recorded responses of neurons in taste cortex to the same tastes on two consecutive days.

      Strengths:

      The authors collected high-resolution behavioral data from the same animals across multiple days, allowing for a detailed characterization of individual variation in taste preferences. They then performed recordings from the same set of animals in response to the same stimuli, allowing them to draw parallels between behavioral and neural responses.

      Weaknesses:

      (1) Recordings were performed only after multiple days of preference testing, and preferences were not tested in between/following recording sessions. This design precludes a direct comparison between neural and behavioral responses.

      (2) Similarly, correlations between neural responses and behavioral measures are not analyzed/reported on an animal-by-animal basis.

    3. Reviewer #2 (Public review):

      Summary:

      The study from Maigler et al investigates how between- and within-animal differences in taste preference relate to differences in neural responsiveness. The experiments rely on an elegant combination of behavioral assays to measure preference (e.g., repeated brief access testing, BAT) and electrophysiological recordings to monitor the activity of ensembles of neurons in the gustatory cortex (GC) of rats.

      BAT with distinct batteries of tastants revealed pronounced variability in preference (measured as licking bout size) across individuals. This variability across individuals persisted after repeated testing. Repeated BAT also revealed that each individual rat's preference for different tastants changed across time.

      Electrophysiological responses of GC neurons to batteries of tastants showed that firing in the "late epoch" of taste processing (i.e., 500ms post taste delivery) correlated more strongly with the individualized rat's BAT preference rather than with a canonical preference ranking. Importantly, this correlation was stronger for the last BAT session compared to the first. Finally, the authors show that the correlation disappeared in a second, consecutive recording session, indicating that exposure to tastants reconfigure preferences.

      Strengths:

      (1) The experimental design allows for an unprecedented look at the relationship between individual variability in taste preferences and neural processing.

      (2) The study demonstrates that taste preference variability is not mere experimental noise but reflects the dynamic nature of taste. A key strength is the clear evidence that behavioral variability is reflected in neural activity patterns, establishing a strong correlation between brain and behavior.

      (3) The evidence that simple exposure to familiar tastes can reconfigure preferences and taste representations is interesting.

    4. Reviewer #3 (Public review):

      Summary:

      Maigler & Lin et al present a convincing set of behavioral and electrophysiological experiments and analyses exploring how individual differences in taste preference map onto neural responses in the gustatory cortex (GC). They go on to examine how both preferences and neural responses shift following intervening taste experience. Their experiments are strengthened by examining tastes of distinct identities and palatability (sweet, sour, salty, bitter) and correspond each animal's individual preference to the palatability-related late phase of the neural response.

      Strengths:

      (1) They demonstrate a relationship between the behavioral expression of taste preference and palatability-related GC neural responses. The direct correlation of expression of taste preference with GC neural responses indicates that taste preference behavior may be less noisy than previously thought, reflecting actual neural activity.

      (2) They address the stability of individual taste preference by comparing within and between session expression. This finding indicates that individual preference on any given trial or test session can differ from canonical palatability.

      (3) The animal's preferences for various tastes are reflected in the neurophysiological recordings, despite that behavior and physiology sessions are separated by several weeks, with an intervening surgery, across multiple delivery methods (active licking vs passive delivery), and across homeostatic states (thirsty vs sated).

      (4) They provide evidence that representational drift in palatability coding may arise from sensory experience rather than from the passive passage of time.<br /> The findings are novel and impactful, and the results are relatively complete.

      Weaknesses:

      (1) The authors state that "no differences in effects were observed between taste batteries" (Methods), but it is not clear which analyses were performed to determine this, especially considering that many of the analyses are within-animal. Without more clarity, it is difficult to evaluate whether the interaction of different tastes within the sets of stimuli bias the main conclusions.

    5. Author response:

      The following is the authors’ response to the previous reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      The authors collect extensive behavioral data and show that preference vary between animals and days, but little insight is provided into what underlies these changes and to what extent they reflect "preference". Two animals drank equal amounts of sucrose and quinine on day one of preference testing, suggesting that behavior does not reflect preference but (lack of) habituation to/proficiency with the testing environment.

      Rats underwent several days of habituation and were checked for lick proficiency before testing. A note regarding this fact has been added to the text, and to the fact that day one consumption of NaCl and water was high in the rat that did not lick much for sucrose, making it clear that the rat was proficiently licking for tastes that it wanted (lines 102-104). We have also added prose to the Discussion section about what lick data tells us about preferences (lines 360-371).

      Recordings were performed only after multiple days of preference testing, and preferences were not tested in between/following recording sessions. This design precludes a direct comparison between neural and behavioral responses.

      The proposed experiment is suggested as a future direction (lines 389-393); performing this experiment is currently impossible, as the experimenters involved have moved on.

      Similarly, correlations between neural responses and behavioral measures are not analyzed/reported on an animal-by-animal basis.

      Although this is an excellent idea, the limitations of the current dataset render the analysis impossible. This is now explained in the Discussion (389-393).

      Reviewer #3 (Public review):

      The authors state that "no differences in effects were observed between taste batteries" (Methods), but it is not clear which analyses were performed to determine this, especially considering that many of the analyses are within-animal. Without more clarity, it is difficult to evaluate whether the interaction of different tastes within the sets of stimuli bias the main conclusions.

      Alas, we are unable to address this concern, although the robustness of our results renders it unlikely that we are observing a phenomenon that is particularly sensitive to specifics of the taste battery.

      It is not clear how the analyses in Figure 3 emerge from the behavioral patterns across Figures 2A2 and 2B2. Along these lines, citric acid responses for R1 and R2 and saccharine responses for R7 and R8 are not shown in Figures 2A2 and 2B2, thus, we cannot determine if they change from the first to final BAT sessions. Salt, even at low concentrations, may not be palatable when rats are in a water-restricted state. It would be interesting to see Figure 3's analysis exclusively for sweet or for bitter tastes.

      We have clarified the text in the results (line 149), methods (lines 531-548), and figure legend (lines 639-644) to better explain Figure 3. The figure itself was updated such that each taste is now color coded, and an analysis looking exclusively at sweet or bitter tests has been run (and found nonsignificant; lines 157-158).

      A potential reason why a single taste experience session causes taste palatability responses in GC neurons to revert to a reflection of canonical palatability ranks, as is seemingly shown in Figure 6, is not discussed.

      This prose, which we apologize for failing to include previously, has been added to the Discussion section (lines 364-371).

    1. eLife Assessment

      This study proposes that fitness level, but not sex, influences exercise-induced hypoalgesia in women. However, the evidence to support this claim is incomplete: the conclusions rely on a small-effect interaction that emerges only under specific conditions, the findings are inconsistent across pain modalities and stimulus intensities, and the absence of a baseline condition limits the interpretability of results as reflecting true hypoalgesia. Additionally, the methods by which fitness level was categorized across cohorts can be questioned. The results of this study could be useful to improve future experimental designs exploring these factors in more depth.

    2. Reviewer #2 (Public review):

      I appreciate the substantial amount of work the authors have invested in revising the manuscript. The response is extensive, and the addition of pooled frequentist and Bayesian analyses represents a considerable and technically sophisticated effort to address the concerns raised during review. The authors have also improved the use and presentation of the continuous pain-rating data and have responded constructively to several methodological points.

      Nevertheless, with respect to my original review, I do not think the revisions have yet aligned the manuscript's central claims with the specificity and uncertainty of the evidence. The additional analyses provide some evidence consistent with a contribution of fitness to heat-pain modulation, but they cannot fully resolve the limited internal replication, selected sample or differences in measurement procedures between studies. Nor do they establish that sex has no effect on pain modulation. In my view, the evidence supports a modest, exploratory conclusion that fitness may contribute to variability in heat-pain modulation under specific conditions, rather than the more categorical conclusion that exercise-induced pain modulation depends on fitness but not sex. The manuscript would therefore be strengthened by reflecting this distinction more consistently in the title, abstract, Discussion and Conclusion. My remaining concerns, organised in relation to the original review, are detailed below.

      (1) The core interpretation may not be fully supported by the data

      (1.1) Lack of baseline condition

      I am satisfied with the authors' substantive response to Comment 1.1, however I note that the authors are still attributing their findings to "hypoalgesia" in the Discussion.

      (1.2) Lack of internal replication across conditions

      The pooled analyses do not resolve the original lack of internal replication. The substantive effect remains largely restricted to heat pain at the highest stimulus intensity and is not clearly reproduced across stimulus intensities or for pressure pain. The authors' response describes the evidence more cautiously, stating that fitness "contributes to variability in exercise-induced pain modulation". However, the title and principal conclusions do not reflect the exploratory, modality-specific and statistically uncertain nature of the evidence.

      (1.3) Deviations from the original study and selective use of data

      I appreciate the addition of analyses using the continuous pain ratings, which addresses my concern that the richness of the data from the current study was not being fully used. However, the decision to pool the current and previous datasets introduces a related and potentially more consequential comparability problem. The previous study used retrospective post-stimulus ratings, whereas the current study used continuous ratings from which peak values were extracted. Rescaling these measures to a common numerical range does not establish their measurement equivalence. This is particularly important because study membership is closely confounded with the participant characteristics central to the manuscript: the higher-fitness female group was recruited and assessed in the current study, whereas the comparison groups were assessed using the earlier procedure. Consequently, apparent effects of fitness may partly reflect differences in outcome acquisition between studies. Indeed, including study membership improves model fit substantially more than including fitness.

      (1.4) Over-reliance on null results regarding sex differences

      I do not think the new Bayesian analyses resolve my original concern regarding the interpretation of the null sex effect. The authors state that the analyses provide no evidence for a moderating role of sex, but they do not report a Bayes factor clearly supporting the null hypothesis for the exercise intensity × sex interaction. In fact, Figure 5 clearly shows that the association between fitness and pain modulation is significant in males but not females. Panels D-F indicate uncertainty regarding a differently specified exercise intensity × sex interaction, rather than evidence for its absence. The figure therefore suggests that the role of sex remains unresolved and may qualify the association attributed to fitness. The authors should therefore distinguish absence of evidence from evidence of absence and temper the conclusion that exercise-induced pain modulation depends on fitness "but not sex".

      (2) Limited sample and lack of diversity

      The manuscript does not acknowledge the limited generalisability of this small, highly selected sample of physically active women recruited primarily from cycling clubs. Although the rebuttal states that the findings should be interpreted within the context of the sample studied, this qualification is absent from the Limitations section. It should be added, and the conclusions restricted accordingly.

      (3) Methodological choices limit interpretability of the data

      (3.1) The use of a non-standard VAS scale (0-150 with a fixed pain threshold at 50) is unconventional and may influence how participants report pain, while limiting comparability with related literature.<br /> The authors' response explains the rationale for the modified scale but does not fully address its implications for measurement comparability. In particular, setting all ratings below 50 to zero is not merely rescaling: it collapses distinct responses into a single value and may alter the distribution of the outcome. The cited study addresses the relationship between real-time and retrospective ratings, but does not validate this scale or transformation.

    3. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The current study is a follow-up to a previously published study by the same research group (Nold et al. 2025). In the previous study, the authors had included a set of exploratory analyses which assessed the effects of fitness level (denominated by a relative FTP), sex, and drug treatment (Naxolone versus placebo). In this previous study, the authors state that "exploratory analysis showed a significant main effect of fitness level on differences in pain ratings in the [saline] condition... suggesting increased hypoalgesia with increasing fitness levels, pooled across all stimulus intensities".

      In the current study, the authors have recruited an additional 22 female participants (21 included in analysis) from local cycling clubs to assess if fitness level does indeed impact exercise-induced hypoalgesia responses to experimental thermal and pressure pain models.

      Strengths:

      The current study has the potential to present a convincing argument about the effect of fitness level and potentially other factors (e.g., sex) on exercise-induced hypoalgesia responses. Combining data across two of their primary studies would be highly fruitful to the research community interested in this area. Specifically, it has the potential to inform sports medicine practitioners and how they administer exercise protocols to help those experiencing pain with a further consideration for the fitness level (and maybe sex) of their patients.

      Weaknesses:

      However, the current study makes several bold claims about the role of fitness level and sex on exercise-induced hypoalgesia, which I do not believe that this study on its own - or in conjunction with the previously published study by the same authors - can make at present. Namely, the current study does not appear to conduct any specific analyses between the cohorts from either study (current and present). The results mention a difference in the group mean values in "fitness level" between cohorts, but the analysis itself on pain responses/exercise-induced hypoalgesia is limited only to the cohort from the current study. If the authors wanted to provide a convincing argument that fitness level has an effect on exercise-induced hypoalgesia, then the analysis of this study would have to include an analysis between the groups considered to be of "high" and "low" fitness level. I do not think the current study does this. Instead, it makes an assumption from the previous study (Nold et al. 2025) which only states that "exploratory analysis showed a significant main effect of fitness level on differences in pain ratings in the [saline] condition... suggesting increased hypoalgesia with increasing fitness levels, pooled across all stimulus intensities". The analysis of this study would have to include fitness level "high fitness" versus "low fitness" of participants across both studies in its statistical model to properly discern if fitness level has an impact on exercise-induced hypoalgesia.

      A similar comment can be made with respect to sex differences, as these have not been assessed in the analysis of this study either.

      Another area of weakness in this study is how "fitness level" has been demarcated across participants. One issue is how authors have assumed that the current cohort is 'fit', whereas the previous cohort was 'less fit', meaning that the authors could be coming to false conclusions about fitness level. In detail, figures within the current study show a large overlap between the 'fit' and 'less fit' cohorts, where some participants have a higher relative functional threshold power (FTP) in the 'less fit' cohort than the 'fit' cohort and vice versa. Therefore, I believe the authors should better demarcate between those that are in the 'more fit' and 'less fit' groups according to a validated and well-established criterion from the kinesiology and sport science literature. That being said, I think this may be problematic in some ways as FTP is considered a relatively poor measure to denote fitness levels, a limitation highlighted in the previous study's review.

      Altogether, whilst I commend the researchers on their body of work across the two studies, the current methods and analysis provide an incomplete assessment of their primary research question, and therefore, I would urge the authors to reconsider some of their methods/analysis and the framing of their results to better reflect the main research question they have attempted to answer. Likewise, I would recommend that readers ensure they consider the current results with caution until the authors have addressed some areas of concern which currently limit their main conclusions.

      We sincerely thank the reviewer for their thorough and insightful comments and for the time invested in reviewing our work. In response, we have made several important revisions, particularly to address the major concerns regarding fitness level and sex differences.

      Specifically, we have:

      (1) Conducted additional pooled analyses to directly evaluate fitness and sex effects.

      We pooled the data from the previous and current studies and conducted linear mixed-effects models investigating the effect of exercise intensity between the subgroups, as well as specifically testing the effect of fitness level and sex across the pooled data. This allowed us to directly test the effects of fitness level and sex on a common scale rather than infer these effects from separate cohorts. To quantify evidence both for and against the presence of exercise-induced pain modulation and its moderation by fitness, sex, and study sample, we estimated Bayes factors comparing nested models of increasing complexity. In addition, we examined the posterior distributions of the regression coefficients (β) and subgroup- specific (males from previous study, females from previous study, females from current study) exercise effects to estimate the magnitude and uncertainty of these effects. The Bayesian analyses provided some evidence that fitness level moderated exercise-induced pain modulation, but sex did not. Importantly, these findings were consistent with the overall pattern of results obtained in the frequentist analyses. All corresponding statistics in the main manuscript and Supplementary Materials have been updated accordingly. To improve transparency, the pooled analyses are now presented in the dedicated Results subsections and accompanied by new figures (Figures 4 and 5). Furthermore, we have adapted the Title, Discussion and Conclusion to more closely reflect the results.

      (2) Clarified the assessment of fitness level.

      We expanded our consideration of fitness level and training status by discussing training volume as an additional indicator of training status. We also acknowledge that fitness is a continuous construct and that some overlap in FTP values exists between cohorts. To address this concern, we conducted pooled analyses using FTP as a continuous predictor rather than relying solely on cohort membership. For consistency and comparability with the previous study, FTP remained our primary indicator of fitness level, while analyses using training volume are now reported in the Supplementary Materials.

      (3) Expanded the mechanistic background on the effect of fitness level and sex on EIH.

      We expanded the mechanistic background in both the Introduction and Discussion to provide a clearer theoretical rationale for how fitness level and sex may influence exercise-induced hypoalgesia (EIH), drawing on relevant experimental and clinical literature.

      Reviewer #2 (Public review):

      This study addresses an important question regarding exercise-induced modulation of pain in women, but the conclusions appear to be based on relatively limited and selective evidence. The authors report an interaction between exercise intensity and stimulus intensity, which they interpret as evidence for exercise-induced hypoalgesia and conclude that fitness, but not sex, modulates this effect. However, this main result relies on a relatively small interaction that emerges only under specific conditions, with inconsistent findings across pain modalities and stimulus intensities, and an analysis approach that does not fully exploit the continuous pain ratings collected. The lack of a baseline condition further limits the interpretability of the findings as reflecting hypoalgesia, and overall, the data provide a rather constrained basis for drawing broader conclusions.

      Strengths:

      (1) The focus on women is important and timely, particularly given the ambiguity in prior findings and the historical bias toward male-dominated samples.

      (2) The attempt to revisit previous findings in a new cohort is valuable in principle.

      Weaknesses:

      (1) The core interpretation may not be fully supported by the data

      The central claim-that the results demonstrate exercise-induced hypoalgesia and its dependence on fitness but not sex-does not appear to be fully supported by the evidence presented.

      (1.1) Lack of baseline condition

      The absence of a no-exercise baseline substantially limits interpretation. The study compares high- and low-intensity exercise, but without a baseline, it is not possible to determine whether either condition produces hypoalgesia or hyperalgesia relative to calibration. The observed HI-LI difference, therefore, reflects only a relative contrast between exercise intensities, not an absolute reduction in pain. As a result, attributing the findings to "hypoalgesia" may be difficult to justify fully.

      (1.2) Lack of internal replication across conditions

      The reported effect is highly specific and does not clearly generalise across the experimental design. It emerges significantly only for heat pain at the highest stimulus intensity, with no clear effects for other intensities and for pressure pain. Moreover, the main statistical result is a relatively small interaction effect with a modest p value, which translates into a difference of approximately 6-8 VAS units on a 150 scale. This combination-a small effect size, limited statistical strength, and restriction to a single condition-substantially weakens the evidence for a robust or generalisable effect.

      (1.3) Deviations from the original study and selective use of data

      Although framed as a follow-up to previous work, the current study introduces substantial methodological changes, particularly in the acquisition and scaling of pain ratings (continuous vs post-hoc ratings, modified VAS with sub-threshold range). Despite collecting rich continuous data, the analysis focuses on peak responses to approximate the previous study. While this may aid comparability, it results in a strong emphasis on a single data point (highest intensity), rather than leveraging the full dataset. This limits both interpretability and comparability.

      (1.4) Over-reliance on null results regarding sex differences

      The conclusion that fitness, but not sex, modulates exercise-induced pain may not be directly supported by the data presented. The current study includes only highly fit women, and comparisons with men or less-fit women rely on non-significant differences in a previous cohort. The absence of a significant difference does not provide evidence for equivalence, and no formal statistical support for a null effect is provided. As such, conclusions about the absence of sex differences would unfortunately benefit from more cautious interpretation.

      (2) Limited sample and lack of diversity

      The dataset is narrow in scope, comprising a small sample (N = 21) of healthy, highly fit women. Key demographic characteristics (e.g. age range, BMI distribution) are not fully presented, explored or discussed. This limits generalisability and makes it difficult to draw broader conclusions about exercise-induced pain modulation in women, as the main focus of the study.

      (3) Methodological choices limit the interpretability of the data

      Several methodological decisions would benefit from stronger justification:

      (3.1) The use of a non-standard VAS scale (0-150 with a fixed pain threshold at 50) is unconventional and may influence how participants report pain, while limiting comparability with related literature.

      (3.2) Participants explicitly reported expecting exercise to reduce pain, introducing a potential confound that is not presently addressed.

      (3.3) A more comprehensive use of the full time series of pain ratings would provide a stronger and more transparent basis for interpretation of the present findings.

      We sincerely thank the reviewer for their time and effort invested in reviewing our manuscript and appreciate the comments. In response to the points raised by the reviewer, we have now updated our work. Specifically, we have:

      (1) Pooled samples and strengthened inferential framework:

      We pooled the current and previous datasets to provide a stronger basis for the primary interpretation of the findings. On this combined dataset, we conducted both frequentist linear mixed-effects models and complementary Bayesian analyses, allowing us to directly test effects of fitness level, sex, and exercise intensity within a unified framework and to quantify both effect sizes and evidence for/against key hypotheses. The Title, Results, Methods and Discussion have been adapted to more closely reflect the results.

      (2) Addressed sample limitations through pooled analysis:

      To mitigate limitations related to sample size and composition in the individual studies, we extended all primary inferential analyses to the pooled dataset. This approach increases statistical power, improves the stability of estimates, and enables a more reliable examination of between-group effects across fitness level and sex, thereby strengthening the generalisability of the findings.

      (3) Expanded analyses of continuous pain ratings:

      To address methodological concerns regarding the use of a summary pain metric, we supplemented the primary peak-based analyses with additional linear mixed-effects models on the full time-series data. Specifically, we modelled time × exercise intensity interactions for both heat and pressure pain, thereby capturing the dynamic structure of pain responses and ensuring that conclusions are supported by the richness of the continuous rating data. These analyses have been added to the Supplementary Materials.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Synopsis

      The current study is a follow-up to a previously published study by the same research group (Nold et al. 2025). In the previous study, the authors had included a set of exploratory analyses which assessed the effects of fitness level (denominated by a relative FTP), sex, and drug treatment (Naxolone versus placebo).

      In the current study, the authors have recruited an additional 22 female participants (21 included in analysis) from local cycling clubs to assess if fitness level impacts exercise-induced hypoalgesia responses to experimental thermal and pressure pain models.

      All the analysis of this study seems to be specific to the "fit" cohort from this study. No analysis or figures seem to show differences between cohorts from the current and previous studies. For this study, the authors found that maximum pain ratings after application of a thermal stimulus and pressure pain stimulus were lower after a high-intensity exercise compared to a lower-intensity exercise. There was also an exercise intensity × stimulus intensity interaction when thermal stimulations were applied, suggesting that the exercise-induced hypoalgesia response varies across exercise intensities as the stimulus intensity increases/decreases. The same cannot be said for pressure pain according the statistical analysis outputs of this study, although inspection of Figure 3B (top row) does illustrate that at higher stimulus intensities, there is a difference between exercise intensities for maximal pain ratings, whereas there is no difference at lower stimulus intensities (30 and 50 VAS). However, observing the changes in maximum pain ratings (Figure 3C) does show similar changes across all stimulus intensities for pressure pain, unlike thermal pain. Therefore, the authors claim that there is an effect of fitness level on the exercise-induced hypoalgesia response.

      Overall, the authors have completed a neat supplementary study to their previous study. However, there are several areas that I believe should be addressed within the current manuscript.

      First, the authors regularly state "...the hypoalgesic response to exercise depends on fitness level but not on sex". As mentioned earlier, the results section of the previous study states that "exploratory analysis showed a significant main effect of fitness level on differences in pain ratings in the [saline] condition... suggesting increased hypoalgesia with increasing fitness levels, pooled across all stimulus intensities". In this study, the authors do not seem to have completed a direct comparison between the two study cohorts. The results for the current study only seem to discuss the effects of exercise intensity within this study cohort. If this study is to be treated as a stand-alone study, then the data that is presented should make it clear that the effect of fitness level is being assessed. Currently, the results and the figures only illustrate the effects of exercise intensity and stimulus intensity, not fitness level (or sex), which are mentioned in the title, abstract, and most discussions.

      Second, on the topic of sex-related effects, as authors repeatedly state ""...the hypoalgesic response to exercise depends on fitness level but not on sex", I think it is worth noting that this statement would require an a priori assessment of sex differences on exercise-induced hypoalgesia responses. As the previous study (exploratory) and this study (no sex difference analysis) do not include this a priori assessment, I do not think they can conclude that sex is not a factor in the exercise-induced hypoalgesia response, though they can surmise that fitness level may have an influencing role if it was specifically tested in this study. If the authors wish to judge the role of sex, there ought to analysis on this specifically.

      Third, whilst the authors have tested the effects of "fitness level", I urge the authors to consider how they have demarcated between fitness levels in this study and any other (validated) criterion that could be used in place of their current method to distinguish between "fit" and "less fit" participants. To explain the cohort of the current study is judged to be "fitter" than the previous study based on the group mean relative FTP. Yet, observing Figure 1, there are several participants with lower relative FTP in the "fit" cohort than the "less fit" cohort, and vice versa. I would recommend the authors to consult with some kinesiologists/sport scientists about this issue. As the central topic of this study is the effect of fitness level, this warrants careful consideration, as the current method to split the groups is problematic, especially as there is clear overlap between the two groups in the metric that the authors have used (relative FTP).

      Fourth, I think it would be highly beneficial for the authors to add more discussion around the theoretical and mechanistic accounts about why fitness level (and sex) would impact exerciseinduced hypoalgesia responses. The current manuscript provides a nice account of the current literature in terms of the direction of each study's results, yet they do not explore why fitness level or sex has such effects. This would be highly valuable to a reader to set the tone of the study rationale in the introduction and provide more depth to the authors' discussion of their results.

      Below I have highlighted all my comments that have been separated into MAJOR and MINOR comment sections.

      I hope my comments are of use to the authors, and I would like to thank them and the journal for the opportunity to review their paper on a truly interesting and insightful topic.

      We thank the reviewer for their insightful comments and appreciate the time invested in reviewing our work. In the following, we have addressed all comments raised by the reviewer and marked all changes in the manuscript.

      Major Comments

      (1) In my opinion, it would enhance the manuscript if the authors provided a theoretical or mechanistic account of why fitness levels or sex would impact the exercise-related hypoalgesia effects. Though some reasoning about the mechanisms of fitness level and sex is mentioned in the discussion of the previous study (Nold et al. 2025), and even if the overall research findings are mixed at present, I do not think it would be overkill to provide the rationale for why the authors do/do not predict a certain response in the current study after their analyses. On this note, the authors may argue that the manuscript content from L52-61 is an account of the previous literature. However, whilst this literature sets out the directions of previous research findings, as I read this, I do not think there is a theoretical framework to underpin why fitness level or sex would impact exercise-induced hypoalgesia (EIH). References already included in the manuscript should have some discussions around this (e.g., Sluka et al. 2018; Koltyn 2000; Koltyn et al. 2014).

      We thank the reviewer for their comment. We agree that the mechanistic rationale for why fitness level and sex may influence EIH could be described in more detail. Accordingly, we have expanded the Introduction/Discussion to provide a more comprehensive overview of the potential mechanisms underlying these effects, drawing on previous EIH literature (e.g., Sluka et al., 2018; Koltyn, 2000; Koltyn et al., 2014) as well as evidence relating to changes in endogenous modulatory systems due to repeated exercise (Anderson & Shivakumar, 2013; Gajic et al., 2026; Matei et al., 2023; Saanijoki et al., 2022; Song et al., 2022) as well as sex differences in these neuromodulatory systems potentially contributing to sex differences in EIH (Blanton et al., 2021; Castelli et al., 2014; Fullerton et al., 2018; Levine et al., 2021; Loyd et al., 2008; Zubieta et al., 2002).

      (2) The study sets out an aim, but I do not see any (a priori) hypotheses in the intro or methods. If no hypotheses were registered, this can be reported in the discussion and factored into the discussion about why certain results would/would not be expected.

      The reviewer is correct that no formal, pre-registered hypotheses were specified for this study. As this study was designed as a follow-up to our previous study, which reported no exercise-induced pain modulation in relatively less fit females, the present investigation was primarily exploratory in nature and aimed to examine whether exercise-induced pain modulations would be observed in a sample of females with significantly higher fitness levels. We have now clarified this in the discussion and acknowledge the exploratory nature of the study when interpreting the findings.

      (3) Is there a sample size justification, sensitivity analysis, or post-hoc calculation for the additional 21 participants to detect effects for fitness level and sex?

      We thank the reviewer for this question. No formal a priori sample size calculation was performed for the recruitment of the additional 21 participants. The current study was designed as a follow-up to our previous work, in which female participants were, on average, slightly less fit than male participants. To better disentangle the effects of fitness and sex, we therefore aimed to recruit a comparable number of female participants with higher fitness levels. Accordingly, we targeted a sample size similar to that of the female subgroup in the previous study.

      (4) I have a wider question about where there has truly been a test between fitness levels.

      Definition and measurement of "fitness level".

      Is there an appropriate rationale that relative FTP is an appropriate measure to distinguish between fitness levels? Understandably, this is done for consistency between the two studies, and there may be mention of the use of the FTP measure in the original study; however, it would be worth justifying the use of the relative FTP measure to categorise fitness level with previous literature to back this up.

      We thank the reviewer for this very valid and important point regarding both the validity of FTP as a proxy for fitness and the distinction between fitness levels. Previous research has shown that the FTP and FTP20 tests are reliable and convenient methods to estimate approximate measures of VO2max (Denham et al., 2020) and that the FTP test is a useful test for performance prediction in moderately trained cyclists (Sørensen et al., 2019). Furthermore, we argue that FTP represents a continuous performance measure in our studies instead of a categorical variable distinguishing between fit and unfit individuals, and we have strengthened the manuscript by providing a clearer justification for its use as a field-based proxy of endurance fitness, supported by previous literature linking FTP to more direct physiological markers such as VO<sub>2max</sub> (Denham et al., 2020). Since we have used FTP in the previous sample as a measure of fitness level, we maintained this for consistency in the current study.

      We also acknowledge that, despite statistically significant group differences, there is meaningful overlap between the males of the previous study and the females of the current study and that “fitness level” should be interpreted as a continuum rather than a strict categorical separation. Accordingly, the labels “fit” and “unfit” should be interpreted relative to the samples included in the current and previous studies rather than as absolute classifications. To reflect this more accurately, we have revised the manuscript to refer to participants as having higher or lower fitness levels, which should be interpreted within the context of the pooled sample rather than as discrete fitness categories.

      Classification of fitness level.

      In L110-111 - The statistical analysis does show a difference between groups, but Figure 1A does show that there is a crossover with around half the sample from each study cohort. That is, the current study had ~16 participants who had a lower relative FTP than the highest relative FTP from the previous study. When removing the highest participant (outlier) in the previous study, there are still 7 participants (a third of the sample) with a lower relative FTP than participants considered of a 'lower' fitness level than the previous study.

      Essentially, on the group level, there is a statistical difference in relative FTP, but there is notable overlap between the two groups.

      We thank the reviewer for this observation. Our aim was not to create entirely non-overlapping fitness categories, but rather to recruit a female sample with significantly higher average fitness levels than the females included in the previous study. As shown in the revised manuscript, females from the current study had, on average, significantly higher relative FTP values than females from the previous study, whereas no significant difference was observed between females from the current study and males from the previous study. We also report training volume as an additional indicator of training status; however, because training volume was self-reported, we consider FTP to provide a more objective and sensitive measure of fitness. The partial overlap in FTP values between samples is not unexpected and, in fact, facilitates comparisons across studies by ensuring that the samples are not entirely distinct populations.

      Could another distinction be made based on:

      - A set value (e.g., 2.0 W/kg-1);

      - Another value from previous literature, and the analysis be re-run with this classification of participants? The approach to use a set value is consistent with kinesiology literature that uses set values to demarcate fitness level for other more common, measures like VO2max (see ACSM guidelines and de Pauw et al. 2013);

      - Or by dissociating between training volume and slightly tweaking the wording to "training status/volume" (although I am cognisant that this would introduce an incongruence in terminology between the authors' studies, but other studies that have been referenced [e.g., Geva and Defrin, do specifically test between training volume]).

      We thank the reviewer for their suggestions. We would like to refrain from making another distinction based on a set value and dividing the group into “fit” and “unfit” individuals. Instead, we have pooled the samples and treated FTP as a continuous variable, which preserves statistical power and avoids the loss of information associated with dichotomising a continuous measure.

      To address the reviewers' suggestion of including training volume as a measure of fitness level, we have repeated our new analyses, namely the LMER model using training volume as a predictor for difference pain ratings (LI – HI Exercise pain ratings), in the pooled sample. These analyses yielded no significant effects for training volume, the interaction of training volume x sex, or training volume x subgroup in either heat or pressure pain. Furthermore, the training volume is potentially influenced by outliers in the current study sample. For completeness, we have added these analyses and according visualisation to the Supplements (Fig. S6).

      We believe that the FTP provides a more sensitive and objective measure of cardiovascular fitness level in our study, as it is not susceptible to recall bias, differences in training intensity and differences in training efficacy. We also note that other studies (Geva & Defrin, 2013) did not rely solely on self-reported training volume as a measure of fitness level, but recruited participants based on training history and athletic status (e.g. competitive triathletes vs. active controls).

      For these reasons, we consider the FTP the most appropriate primary indicator of fitness level in the current (and previous) study, while still reporting training volume in the main manuscript to consider.

      The above suggestions would also allow to select matched pairs from the male cohort when comparing effects between sexes. Altogether, though, I believe this comment to be the most important in my review of this paper, as the primary question depends strongly on how fitness level is defined and categorised by the researchers, and this requires careful consideration and validation from previous literature if such decisive conclusions are to be drawn from the results. Therefore, a clearer description of the reason why certain effects are expected (theory/mechanisms) and how participants have been categorised will be highly valuable for readers. It may be useful for the authors to reach out to kinesiologists/sport scientists within their peer groups to help with this comment.

      To further address the concern regarding between-cohort comparability, we have now pooled the samples from the previous and current study and re-analysed the data fitness level as a continuous variable rather than relying on cohort-based comparisons. Specifically, we conducted regression analyses examining fitness level, sex, and their interaction, as well as subgroup analyses including males from the previous study, females from the previous study, and females from the current study. These analyses were complemented by Bayesian model comparison and posterior inference. The results are now reported in the Results section. We also acknowledge that fitness is best viewed as a continuum rather than a discrete characteristic and have revised the manuscript accordingly, replacing references to “fit” and “unfit” participants with descriptions of relatively higher and lower fitness levels within the pooled sample.

      Finally, as mentioned in the previous comment (major comment #1), we have expanded the Introduction and Discussion to more clearly outline the mechanistic and theoretical rationale for expecting effects of fitness level and sex on exercise-induced pain modulation, drawing on relevant literature (see major comment #1). We believe these additions substantially strengthen both the conceptual justification and the analytical robustness of the manuscript.

      (5) The abstract states that "...further investigation to determine if and to what extent exercise induced hypoalgesia depends on fitness level and/or sex." However, I cannot see any analysis relating to sex in the results of this study that takes the female sample of this study and the male sample of the previous study and then carries out another LNER model? I also see no report of results about sex in any of the figures/tables. If sex is part of the abstract and main research question, then I would argue it ought to be in the results and feature in figures within the main manuscript, not supplementary material.

      We agree with the reviewer that, given the emphasis on fitness and sex in the Abstract and research aims, analyses directly examining sex effects should be presented in the main manuscript. In response, we pooled the samples from the previous and current studies and repeated the linear mixed-effects regression analyses used in the previous study to examine the effects of fitness and sex on exercise-induced pain modulation. We further complemented these analyses with Bayesian model comparison and parameter estimation to assess the moderating effects of fitness level and sex in the pooled dataset. The results of these pooled analyses are now reported in the corresponding Results section and are presented in the main manuscript as Figure 5. The Abstract, Results, Discussion, and Conclusions have been revised accordingly.

      (6) The current abstract and main manuscript make it seem that this study completed an additional analysis on the effect of sex, but this does not appear to be the case. Statements like "together with our previous findings, this suggests that exercise-induced hypoalgesia depends on fitness level but not on sex" are quite decisive, although it does not seem that any analysis was completed between sexes in an a priori fashion. The only data I can see that assesses between sex effects is an exploratory analysis from the previous study, which was part of a larger exploration of sex - fitness level - and drug interactions.

      Therefore could the authors either:

      - Run this analysis between sexes;

      - Or make it explicit that this study did not conduct any specific a priori analysis on sex differences in EIH at high and low intensities.

      - And/or temper how decisive their conclusions on the (null) effects for sex, unless there are statistical analyses to support this.

      We thank the reviewer for this suggestion. We agree that the manuscript did not include a dedicated analysis of sex effects in the combined dataset. In response, we pooled the samples from the previous and current studies and conducted additional frequentist (LMER) and Bayesian (Bayes Factor and posterior distributions) analyses examining the effects of fitness and sex on exercise-induced pain modulation. The frequentist analyses indicated an association between fitness and EIH, but not sex, while the Bayesian analyses similarly provided little evidence for sex-dependent effects. These new analyses have been added to the manuscript, and the Abstract, Results, Discussion, and Conclusions have been revised accordingly.

      Section-specific Comments:

      (1) Abstract

      L38-40: See major comment #6 above.

      As described above, we have now conducted additional analyses on the moderating effect of fitness level and sex using frequentist and Bayesian analyses and adapted the Abstract accordingly.

      (2) Introduction

      L68 - What is meant by "robust" in this sentence? Robust to describe the direction and magnitude of the differences in effects between sexes seems a little misrepresentative.

      We thank the reviewer for their comment and agree that the wording is vague. To emphasise clarity, we have rephrased this to “[…] whereas others (Koltyn, 2000; Sternberg et al., 2001; Vaegter et al., 2014) suggest that hypoalgesia following exercise is more pronounced in females, but overall results remain inconclusive (Rice et al., 2019).”

      (3) Results

      L124 - What is the online rating scale? It is the word "online" in particular that is confusing here, as it is described in the methods, but just seems to be a standard handheld device with a cursor.

      We thank the reviewer for their comment. In this case, “online” refers to the continuous ratings whilst the stimulus is ongoing, using a handheld rating device and using button presses. We clarified this further at the beginning of the Results and Methods sections to include “real-time (continuous) pain ratings” to avoid confusion.

      L136 - I think there is a missing word here before the set of parentheses. RPE?

      We thank the reviewer for detecting this and have added “RPE” before the parentheses.

      L136-138 - I would amend to read something like... "This confirms the exercise intensity protocols elicited a different output, relative power, physiological demand (HR), and perceived demand (RPE)", just to be clear on what the differences have been physiologically and psychologically for the participants when exercising at the two intensities.

      We agree with the reviewer and have rephrased the sentence according to their suggestion for clarity.

      (4) Discussion

      L190 - "results suggest that fit females show a hypoalgesic response to HI compared to LI exercise". I think there is a word missing in this sentence to make it clear what the results are.

      We have revised the sentence in accordance with the new (pooled) analysis results.

      L250-253 - The authors have given a fantastic account of the previous literature and the trends that each study has observed. However, in line with major comment #1, could the authors include some discussion to speculate why there are expected differences between training level/fitness level and sex (plus interactions)? Some studies that are referenced are theoretical papers, and even if the current literature is unsure about the likely mechanisms behind fitness and sex differences in EIH responses, stating this would also be useful to a reader but the authors could signpost them to specific references that would broaden their understanding after reading this study.

      We have now provided a more detailed account of the potential mechanistic background on why fitness level and/or sex could influence exercise-induced modulation in the Discussion (see major comment #1).

      (5) Limitations

      L258-259 - If I have understood this sentence correctly and the content in L301-304, the heat stimulus (temperature of the thermal probe) that was applied to induce a 30 VAS pain intensity rating at rest actually induced no pain at all, as 30 VAS is considered below the pain threshold of "minimally painful". In retrospect, this seems like an odd choice of measurement.

      It means that 30 VAS is simply a measure of warm (non-painful but detecting heat) and perturbation to the skin (pressure but not hurting) in the heat and pressure pain conditions, respectively. Therefore, to include the 30 VAS values in the analysis to dissociate whether HIGH or LIGHT intensities, fitness level, or sex have an impact on EIH seems misplaced, as there is no actual pain for which a hypoalgesia response can be tested at this intensity.

      Making this clearer in the results or discussion would be worthwhile for the readers, in my opinion.

      We thank the reviewer for highlighting this. In both the previous and current studies, participants were calibrated using the same procedure and rating scale (0 – 100) to identify painful stimulus intensities corresponding to VAS 30, 50, and 70. On the main experimental day, participants in the current study rated on a modified scale ranging from 0 to 150, with 50 marking the pain threshold, such that ratings below 50 reflected a non-painful rating. Importantly, the calibrated VAS 30, 50, and 70 stimuli refer to the target pain intensities established during the calibration procedure and not to the numerical values displayed on the experimental rating scale. Participants were familiarised with the scale and anchor points prior to testing and viewed only the visual analogue scale rather than numerical values during the experiment. We have adapted the wording, referring to VAS 30, 50, and 70 as intensities and not numerical ratings.

      (6) Conclusion

      L276-277 - Similar mention as before, can the authors please make it clear if there is a sex effect in their analyses or how this has properly been tested in a prior study through an a priori assessment to be certain that "fitness level rather than sex plays a central role in modulating EIH".

      We have now tested the effect of sex in frequentist and Bayesian analyses in the pooled samples and revised the conclusion in accordance with the results.

      (7) Methods

      L284 - What denotes an individual being "physically active"?

      We thank the reviewer for this question. “Physically active” was defined based on training volume, operationalised as regular participation in structured cycling training sessions (i.e., consistent engagement in planned cycling training units per week). This criterion was used to ensure that participants had an established and ongoing training routine relevant to the study context. We have clarified this in the referenced section.

      Table 1 - Female participants in the current study are noticeably older than the other cohorts from the previous study. While all are considered "young" (<40 y.o.), if one were to take a matched pair of females from each cohort (previous vs current) in terms of relative FTP, then the years of experience and exposure to training are likely a strong influence on EIH responses, not "fitness level" as it is described in this study. Just a speculative thought.

      The reviewer raises an interesting point. However, since we have not acquired any information on the years of training and volume, we are unable to answer this question in the current framework. Future research should investigate the effect of training years (which potentially strongly correlates with fitness level) on exercise-induced pain modulation.

      L313-315 - For future reference, there are some nice papers on how to administer the RPE scales with clear instructions to be certain that perceived effort is being measured appropriately (Halperin & Emanuel 2020; Halperin & Visgotsky 2025; Malleron et al. 2023). I always have a habit of signposting to these studies with regard to RPE because it can often be misrepresented in many different areas of research where RPE scales are used.

      We thank the reviewer for this suggestion and for highlighting these relevant papers. In the present study, participants were extensively familiarised with the rating of perceived exertion scale, and the instructions closely followed the original definition and recommendations provided by Borg (1998). The scale was presented in accordance with its intended anchor points to promote accurate reporting of perceived exertion. We have now added a citation (Borg, 1998) in the Methods section to clarify this procedure.

      (8) Code Availability

      Thank you for including open source code in your submission. I am very appreciative of researchers who champion open research principles.

      We appreciate the reviewer’s comment and support of open research practices. We have updated the code containing the updated analyses and datasets.

      Reviewer #2 (Recommendations for the authors):

      (1) Core interpretation may not be fully supported by the data

      We thank the reviewer for this comment. To investigate the role of fitness level and sex in exercise-induced pain modulation, we have now pooled the samples from the previous and current study and conducted frequentist (linear mixed effects models) analyses. Specifically, we tested for the effect of exercise intensity between the subgroups, as well as (consistent with those conducted in the previous study (Nold et al., 2026) testing the effect of fitness level and sex across the pooled data. This allowed us to directly test the effects of fitness level and sex on a common scale rather than infer these effects from separate cohorts. Furthermore, we conducted Bayesian analyses to quantify evidence both for and against the presence of exercise-induced pain modulation and its moderation by fitness, sex, and study sample, utilising the Bayes factors comparing nested models of increasing complexity. In addition, we examined the posterior distributions of the regression coefficients (β) and subgroup-specific exercise effects to estimate the magnitude and uncertainty of these effects. The Bayesian analyses provided some evidence that fitness level moderated exercise-induced pain modulation, but sex did not. Importantly, these findings were consistent with the overall pattern of results obtained in the frequentist analyses. All corresponding statistics in the main manuscript and Supplementary Materials have been updated accordingly. To improve clarity, the pooled analyses are now presented in dedicated Result subsections and accompanied by new figures (Fig. 4 and Fig. 5).

      (1.1) Lack of baseline condition

      We thank the reviewer for this important comment. We agree that, in the absence of a no-exercise control condition, our design does not allow us to determine whether either exercise condition produced hypoalgesia or hyperalgesia relative to no exercise. Rather, the observed differences reflect the relative effects of high- versus low-intensity exercise on pain ratings.

      Our study was not designed to assess the overall effect of exercise compared with rest, but rather to investigate whether exercise intensity influences exercise-induced pain modulation. Specifically, we hypothesised that high-intensity exercise would produce greater pain modulation than low-intensity exercise. This approach was motivated by previous work demonstrating intensity-dependent differences in post-exercise pain responses (Jones et al., 2019; Vaegter et al., 2014).

      In response to the reviewer's comment, we have revised the manuscript to clarify this distinction and have replaced several instances of "exercise-induced hypoalgesia" with the more neutral term "exercise-induced pain modulation" where appropriate. We also explicitly acknowledge in the Limitations that the lack of a no-exercise condition precludes conclusions regarding absolute hypoalgesic/hyperalgesic effects of either exercise condition.

      (1.2) Lack of internal replication across conditions

      We thank the reviewer for this comment. We agree that the observed effect was most pronounced for heat pain at the highest stimulus intensity and was not consistently observed across all stimulus intensities or pain modalities. Importantly, however, this pattern mirrors the findings of our previous study, in which exercise intensity effects were likewise evident for heat pain but not pressure pain. We therefore interpret the current findings as a replication of the modality-specific pattern observed previously rather than an isolated positive result.

      To further address concerns regarding robustness and sample size, we have now pooled the data from the previous and current studies and conducted additional frequentist and Bayesian analyses examining the moderating roles of fitness level and sex on exercise-induced pain modulation. These analyses yielded converging evidence that fitness level contributes to variability in exercise-induced pain modulation, thereby strengthening confidence in the overall findings.

      With respect to pressure pain, previous meta-analyses have reported comparable or even larger exercise effects for pressure pain than heat pain (Naugle et al., 2012). However, most studies included in these analyses employed handheld pressure algometry, whereas our study used cuff pressure stimulation. Consequently, the pressure pain measures used here may not be directly comparable to those reported in the broader literature.

      (1.3) Deviations from the original study and selective use of data

      We agree with the reviewer that we have adapted the rating scale to reflect a real-time pain rating. To provide a comprehensive account of participants’ pain experience, we have included analyses corresponding to the visualisations on how online ratings evolve over the course of the pain administered in Supplemental Figure S7 – S8. Specifically, we conducted LMER models with exercise intensity × time on heat and pressure pain ratings, separately (Supplemental Fig. S7). The results yielded a significant interaction of exercise intensity × time on heat (β = -0.13, SE = 0. 06, t(7577) = 2.12, P = 0.03) but not pressure pain ratings (β = -0.07, SE = 0.10, t(7956) = -0.71, P = 0.48). Furthermore, we conducted LMER models with exercise intensity × time × stimulus intensity on heat and pressure pain ratings (Supplemental Fig. S8), suggesting no significant interaction on pressure pain ratings (β = -0.0002, SE = 0.004, t(2353) = -0.06, P = 0.95) but a significant interaction on heat pain ratings (β = -0.01, SE = 0.003, t(2201) = -3.24, P = 0.001).

      As the reviewer correctly notes, to facilitate comparison with the previous study, we conducted our primary analyses using peak pain ratings. We acknowledge that this approach does not fully capture the richness of the continuous data but allows for comparability.

      (1.4) Over-reliance on null results regarding sex differences

      We thank the reviewer for this important point regarding the interpretation of null effects, particularly in relation to sex differences. We agree that the absence of a statistically significant difference does not, in itself, provide evidence for equivalence between groups.

      To address this issue, we have pooled the samples and supplemented our frequentist analyses with Bayesian analyses. Bayes factors were computed to quantify the relative evidence for the null versus alternative hypotheses, providing a continuous measure of support that complements frequentist inference, particularly in cases of non-significant effects where evidence for the absence of an effect is of interest. Furthermore, posterior distributions of the regression coefficient of interest (in this case, the moderating role of sex on exercise-induced pain modulation) provided no evidence for a moderating role of sex on exercise-induced pain modulation. This approach allows for a more nuanced interpretation of the data, especially concerning null effects. We have adapted the Methods, Results, and Discussion accordingly.

      (2) Limited sample and lack of diversity

      We thank the reviewer for this comment. The study was intentionally designed to include only female participants with significantly higher fitness levels compared to the females from the previous study, as our previous study included females who were slightly less fit than males, making it difficult to separate the effects of sex and fitness. We, therefore, aimed to examine exercise-induced pain modulation in a more homogeneous sample. However, much of the EIH literature has been conducted in highly trained male participants (Janal et al., 1984; Saanijoki et al., 2022; Scheef et al., 2012). As such, studies in women of higher fitness are important to improve our understanding of potential sex differences in exercise-induced pain modulation. While the present findings should be interpreted within the context of the sample studied, when analysed and considered alongside our previous work, they contribute to a broader understanding of exercise-induced pain modulation across sexes and fitness levels.

      (3) Methodological choices limit the interpretability of the data

      (3.1) The use of a non-standard VAS scale (0-150 with a fixed pain threshold at 50) is unconventional and may influence how participants report pain, while limiting comparability with related literature.

      We thank the reviewer for their comment. Since we introduced real-time pain ratings in the current study, while the pain is ongoing, we aimed to introduce a scale that allowed us to pinpoint the period when a painful stimulus reaches painfulness (exceeds 50 on the scale). To allow for comparability between the studies, we have calculated the peak pain rating provided during the real-time pain ratings, as this corresponds to the instruction participants received in the previous study to rate the most painful point of the previous stimulation. Previous research (Koyama et al., 2004) has shown that, in heat pain, real-time ratings account for R<sup>2</sup> = 0.89 of the variability of post-stimulus rating, where much of the variability for the post-stimulus intensity pain rating is accounted for by the peak response during the online ratings (adj. R<sup>2</sup> = 0.3 – 0.4), suggesting good comparability of both pain measures. Furthermore, as we have now pooled the samples and conducted analyses, we rescaled the maximum pain ratings so that ratings below 50 (below the pain threshold) were set to 0, corresponding to VAS 0 in the previous study, resulting in pain ratings ranging between 0 and 100.

      (3.2) Participants explicitly reported expecting exercise to reduce pain, introducing a potential confound that is not presently addressed.

      We thank the reviewer for this comment. Indeed, participants reported expecting lower whole-body pain in response to acute aerobic exercise, but no changes in joint or muscle pain. In the context of exercise, previous research (Colloca et al., 2018) showed that exercise-unrelated expectations and exercise itself induce comparable hypoalgesic effects, though they might work through distinct mechanisms. Studies directly manipulating exercise-related expectations showed that education about EIH, compared with general education about exercise and pain, could induce hypoalgesia following aerobic exercise (Jones et al., 2017). Interestingly, another study (Vaegter et al., 2020) investigated the interplay of exercise-related expectations (through verbal instructions) and resistance exercise on pain perception and showed that only explicit negative instructions induced hyperalgesia, whereas positive or neutral exercise-related instructions did not change the magnitude of EIH.

      Since participants in the current study reported highly specific positive expectations only for whole-body pain, but not for joint or muscle pain, expectations are unlikely to fully explain our findings. Consistent with this, in our previous study using the same prospective expectation questionnaire, we found no significant expectation effects for any pain domain (Nold et al., 2025), although we acknowledge that the samples differed, especially with respect to fitness levels.

      Nevertheless, the current study was not designed to disentangle the effects of exercise and expectations, as expectation ratings were measured prospectively but not manipulated. Therefore, we cannot determine to what extent positive expectations of exercise have contributed to the observed effects, and we have now acknowledged this as a limitation in the Discussion.

      (3.3) A more comprehensive use of the full time series of pain ratings would provide a stronger and more transparent basis for interpretation of the present findings.

      We thank the reviewer for this suggestion. As noted in our response to Comment 1.3, we have included analyses and visualisations of the full time series of online pain ratings in the Supplementary Materials (Fig. S7–S8), providing a more comprehensive understanding of pain dynamics over time.

      Figures are very low in quality - artefacts, small text, insufficient labelling.

      We thank the reviewer for highlighting this and, indeed, rendering the PDF reduced the quality of the images. For the reviewed version, we have been specifically attentive to image quality by increasing font sizes and adding labels.

      There was not enough detail in the methods section for the number of cycling blocks, and how low and high intensity were distributed throughout the session.

      We thank the reviewer for their comment. Participants completed four cycling blocks in total, two HI and two LI exercise blocks, presented in a pseudo-randomised order in the session. To improve clarity, we have added the relevant details to the Methods section.

    1. eLife Assessment

      This study presents important findings linking peripheral inflammation to the remodeling of perinodal adipose tissue and draining lymph nodes, suggesting a mechanism by which local tissue inflammation can reshape LN structure and metabolism. The evidence is solid and supported by observations, future studies will address any outstanding questions.

    2. Reviewer #1 (Public review):

      The idea is super interesting, and the subsequent work is potentially significant because it links peripheral inflammation to remodelling of perinodal adipose tissue and draining lymph nodes. This suggests an antigen-independent manner by which local tissue inflammation can communicate with and reshape immune organ structure and tissue metabolism. However, the evidence is suggestive. For instance, many conclusions rely on correlational weight/cellularity relationships, models with confounders (spontaneous wounding; potentially systemic IMQ), and macrophage dependence inferred from a single pharmacologic approach without definitive depletion/lineage or tracer-based causal link.

      Comments on revised version:

      Overall, the rebuttal addresses most of the original concerns constructively, with additional data and appropriate moderation of several claims. However, some of the key mechanistic conclusions remain over-stated. In particular, macrophage origin, the contribution of systemic inflammation, and the mechanistic relationship between fat-pad remodelling and lymph-node expansion remain incompletely resolved. The manuscript would nevertheless be acceptable if these limitations are clearly acknowledged and the mechanistic language toned down. That said, I think the rebuttal is broadly satisfactory.

    3. Reviewer #2 (Public review):

      Summary:

      The authors aim to demonstrate skin inflammation is associated with fat pad atrophy and lymph node expansion. They further propose that these phenotypes are driven by the recruitment and lipid metabolism of CCR2-independent macrophages.

      The authors took advantage of two skin inflammation models, fight-induced and imauimod-induced skin inflammation and analyzed multiple tissues, including skin, fat pads, and lymph nodes. Using a macropahge-depletion method (e.g., CSF-1R inhibitor), the authors further suggest the inverse correlation between fat pads atrophy and lymph node expansion is macropahge-dependent.

      Comments on revised version:

      The authors improved their manuscript with additional data and discussion. However, the authors need to clarify their terminology in the experiments. To strengthen their conclusion, authors need some additional experiments addressed below.

      Clarify cohousing conditions in Fig. 1 through 3: the authors used 'resting' ,'alone', 'together'. These are unclear. Please state whether 'resting' mice were singly housed or co-housed in Fig. 1. If they singly house, specify when they were separated from their littermate or cage mates. In Fig. 1-3. If they were co-housed ('together'), specify whether their cage mates were littermate or non-littermate Ccr2-/- mice in the manuscript, including line 107.

      Strengthen the link between fighting and the observed phenotypes: Co-housed Ccr2-/- males had increased lymph node (LN) weight and reduced fat pad weight. To test whether these changes are associated with fighting-induced skin inflammation, please consider comparing mice that are singly housed after three weeks of co-housing with mice that are singly housed throughout the same period.

      Clarify the monocyte analysis: At line 141, please use 'monocytes' rather than 'inflammatory monocytes' if the analysis does not include inflammatory markers/phenotypes. Please also label the monocytes and macrophage populations in Fig. 3Di and 3Dii as was done for neutrophils and eosinophils in Fig. 3B.

      Recruitment vs. Self-renewal: At line 144, CD11b expression alone does not distinguish recruited cells from cells expanded by local proliferation; Inflammation itself can alter CD11b expression. Please consider providing the evidence that distinguish these possibilities. Fate mapping would be an ideal approach, while Ki67 staining or BrdU incorporation assay could alternatively assess proliferation.

    4. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Major Comments:

      (1) "Wounding/fighting" evidence is confounding.

      Unless I am mistaken, a large part of the argument for inflammation-driven perinodal fat pad atrophy and LN expansion relies on spontaneous fighting injuries in co-housed CCR2-/- males, including animals "culled...due to excessive wounding." Because wound severity, duration, infection load, stress, and cage dynamics are uncontrolled, isn't it difficult to assign causality to "cutaneous inflammation"?

      We thank the reviewer for this point and fully understand the concerns here. We have carefully gone through the text now and ensured that we refer to wounding-associated inflammation throughout. This therefore takes into account potential wound infections etc. We are also confident that the phenotype relates to cutaneous inflammation because it is only seen in lymph nodes and fat pads draining inflamed skin. We have adapted the discussion accordingly to address this important issue (lines 169-172 of the revised text).

      (2) The "CCR2-independent macrophage" conclusion.

      The manuscript interprets persistence/accumulation of macrophages despite reduced inflammatory monocytes as CCR2-independent recruitment or local proliferation. However, CCR2 deficiency can alter immune baselines and long-term tissue remodelling. Perhaps consider bone marrow chimeras (WT to CCR2-/-, CCR2-/- to WT ????) or an inducible CCR2 deletion approach to separate developmental/systemic effects from acute inflammation-driven mechanisms. If "in situ proliferation" is proposed, include a direct readout (e.g., Ki67 in ATMs in the fat pad).

      We have previously published that CCR2-/- mice display no alterations in any myeloid or lymphoid cell subsets with the exception of Ly6C+ monocytes. We have now referred to this in the revised text (lines 135 and 136) and hope that this addresses the reviewer’s concerns. In terms of whether the CCR2-independent increase in macrophages as a consequence of recruitment or local proliferation, the fact that the macrophages are CD11b+, along with the relative increase in inflammatory monocyte content in co-housed CCR2-/- mouse skin, strongly suggests that they derive from peripheral blood monocytes and therefore from recruited cells. We have clarified these points on page 7 of the revised text.

      (3) IMQ and systemic effects.

      The work relies on topical Aldara/imiquimod as an "inflammation without antigen" driver of distal LN/fat-pad remodelling. But IMQ is well known (and cited by the authors) to enter circulation and drive systemic responses, which could blur whether effects are truly draining-site specific vs systemic metabolic/inflammatory effects. It would be ideal to provide systemic context: plasma cytokines and/or metabolic readouts (e.g., circulating FFAs) to distinguish local vs systemic drivers.

      This is an important point. We have now included plasma cytokine data showing that these are indeed elevated systemically in Aldara-treated mice (new Supplementary Figure 5). We have altered our discussion of this point to reflect these new data (lines 228-229 of the revised text).

      (4) Macrophage dependence is inferred from CSF1R inhibitor treatment.

      However, validation of macrophage depletion and specificity is incomplete. The manuscript uses AZD7507 (CSF1R inhibitor) and observes partial rescue of fat pad/LN phenotype while skin severity (PASI) is unaffected. But, to this reviewer, the data shown do not clearly quantify actual macrophage depletion efficiency in the target fat pad, and LN at endpoint, and CSF1R blockade can affect multiple myeloid populations. Therefore, show absolute macrophage counts (and likely other myeloid populations) in fat pad and LN with/without AZD7507 at the analysed timepoints, not only outcome weights. (The methods describe dosing but not endpoint depletion quantification??)

      We have now added these data (new Figure 7D and Supplementary Figure S6 and discussed on page 13 of the revised manuscript). What we show is that fat pads from Aldara-treated mice show a large reduction in macrophages and also a reduction in dendritic cells, although the number of dendritic cells was already low in the fat pad of Aldara-treated females. The lymph nodes of Aldara-treated females with AZD7507 also show reduced macrophage counts, but the reduction is also seen in all other immune subsets (lymph nodes were smaller so reduced counts in all subsets). We hope that these new data satisfy the reviewer’s concerns.

      (5) Fat pad atrophy/LN expansion is a correlation.

      The paper emphasises negative correlations between fat pad and LN weights/cellularity at baseline and with inflammation. But correlation does not establish whether fat pad lipolysis drives LN expansion, whether LN changes drive fat remodelling, or whether both reflect systemic mediators. Add tissue-level evidence distinguishing true adipocyte loss vs other contributors to "weight change" (e.g., oedema/fibrosis).

      We agree that our data do not establish a direct causal relationship between adipose tissue loss and LN expansion, but the observations do demonstrate a strong temporal association. As seen from the added graph (new Figure 8B and related text on page 13 of the revised manuscript), the rapid and concurrent occurrence of both processes (approximately 25% reduction in fat pad weight and 30% increase in LN size within 24 h of Aldara treatment) suggests that they are closely linked biological events occurring within the same tissue microenvironment. Determining whether these changes arise through direct local interactions or reflect broader inflammatory mediators would require additional mechanistic investigation.

      (6) Evidence for "fatty acid donation" from fat pad to LN.

      The lipid data are described as "exemplary," and the inference that LN fatty acids originate from the fat pad is based on temporal ordering and relative abundance. This does not rule out plasma spillover, LN-intrinsic metabolism, or altered lymph flow.

      We agree with reviewer and have amended our discussion of this point appropriately (page 16 of the revised manuscript).

      Reviewer #2 (Public review):

      The authors aim to demonstrate skin inflammation is associated with fat pad atrophy and lymph node expansion. They further propose that these phenotypes are driven by the recruitment and lipid metabolism of CCR2-independent macrophages.

      The authors took advantage of two skin inflammation models, fight-induced and imauimod-induced skin inflammation and analyzed multiple tissues, including skin, fat pads, and lymph nodes. Using a macropahge-depletion method (e.g., CSF-1R inhibitor), the authors further suggest the inverse correlation between fat pads atrophy and lymph node expansion is macropahge-dependent. While the study identifies this intriguing inverse correlation during skin inflammation, the causal pathway linking fat pad atrophy and lymph nodes enlargement has not been clearly established.

      To improve the rigor of the manuscript, the authors address the following concerns;

      (1) CCR2-deficient mice showed reduced inflammatory monocytes and monocyte-derived macrophages (PMID:16462739; 16341265). During tissue inflammation, CCR2+ classical monocytes are typically recruited to the injured peripheral tissues, including skin, where they differentiate into monocyte-derived macrophages (PMID:38474365). While inflammatory monocytes were reduced in the skin (Figure 3 d), fat pads (Figure 4a, S2D) of CCR2-deficient mice, macrophage numbers were significantly increased in these mice. It remains unclear whether CCR2-independent macrophages were newly recruited from alternative sources or tissue-resident macrophages underwent local self-proliferation to compensate for the loss of CCR2+ monocyte-derived macrophages.

      We agree with reviewer and have expanded our discussion of this important point (see text on pages 7 and 8 of the revised manuscript). As mentioned in response to reviewer 1 we have inferred from the expression of CD11b and c on recruited macrophages that they are derived from peripheral blood monocytes and therefore ultimately are recruited and have not arisen due to local proliferation. We hope that the reviewer is happy with this expanded discussion.

      (2) In line 258, the authors state that there was "a significant reduction in CD11C- CD206+ anti-inflammatory macrophages (Figure 4b i-iii)". However, the quantification data in Figure 4b iii do not appear to show any reduction in anti-inflammatory macrophages in either males or females. Please reconcile this discrepancy between the text and the figure

      We apologise for our carelessness here and this is the consequence of not having normalised some of the data presented. We have now done this and updated the graph which now, hopefully, more convincingly demonstrate reduction in CD11c- macrophages.

      (3) Although CD11C and CD206 were historically used as markers of inflammatory and anti-inflammatory markers, respectively. These markers are no longer considered sufficient to define the macrophage polarization state, particularly in adipose tissue, where they are constitutively expressed by resident macrophages (PMID:34210853). Numerous studies have demonstrated substantial macrophage diversity/heterogeneity across iWAT, eWAT, and brown fat tissues. The authors should discuss adipose macrophage diversity beyond the outdated M1/M2 frame.

      We thank the reviewer for pointing this out and apologise for the superficial way in which we have addressed macrophage heterogeneity in the fat pads. We selected CD11c, as it is clearly an important discriminatory marker for tissue macrophages, and CD206 as it presents itself as a common marker of both CD11c-positive and CD11c-negative macrophages in our analyses. We fully acknowledge that the understanding of macrophages has moved on beyond the simple pro-and anti-inflammatory categories have therefore amended the discussion appropriately (please see text on page 8 of the revised manuscript). We hope that these changes are satisfactory for the reviewer.

      Minor issues

      (1) Clarify which claims are "antigen-independent" vs "no intentional antigen." The introduction/abstract emphasises "absence of antigen presentation," but inflammation models (wounds/IMQ) do not exclude endogenous/self-antigens. Rephrase or add controls aligned to the claim.

      We fully accept reviewer’s point here and have removed references to ‘antigen-independent’ throughout.

      (2) Provide more explicit randomisation/blinding details for subjective readouts (e.g., PASI scoring).

      We agree that additional methodological detail would improve transparency. Inflammation scoring was performed by an independent collaborator blinded to treatment allocation. Animals were not formally randomized between groups because AZD-treated mice required additional gavage procedures and handling distinct from control animals. We have now included additional details in the Materials and Methods section (see page 22 of the revised text).

      (3) Increase transparency on sample sizes and independence when pooling tissues (e.g., pooled fat pads, pooled macrophage RNA samples).

      Lymph nodes and fat pads were used for different analyses throughout the study, resulting in differences in sample processing and pooling strategies between experiments. Information regarding biological replication and any sample pooling is provided within the corresponding figure legends for each experiment.

      In most cases, both fat pads and lymph nodes of each mouse were pooled for analysis, this is specified in each figure legend (i.e. ‘each point represents the combined weight of both inguinal lymph nodes/fat pads for each mouse’

      Pooling of fat pads from multiple individual mice was only done for RNA sequencing due to low numbers of adipose tissue macrophages. Again, details are in the figure legends.

      We hope that this is acceptable to the reviewer.

      (4) Quantify systemic effects of IMQ (weight loss is mentioned in monitoring; include the data and interpret its relevance).

      Body weight was monitored throughout the study for animal welfare purposes but was not systematically recorded for analysis. While we cannot formally exclude systemic effects, we believe the observed phenotype is unlikely to be driven primarily by general sickness behaviour, as substantial tissue changes were already evident by day 1 following treatment, including an approximately 25% reduction in fat pad weight and a 30% increase in lymph node weight (please see revised figure 8B). The rapid onset and tissue-specific nature of these changes suggest an early biological response rather than a secondary consequence of generalized illness.

      (5) Tone down mechanistic language ("donation," "feeds the LN") unless tracer/intervention experiments are added; keep as "consistent with" until proven.

      We thank the reviewer for pointing this out and have amended the language appropriately.

      Recommendations for the authors:

      Reviewer #2 (Recommendations for the authors):

      Minor concerns;

      (1) Please delete 'i-iii' from Figure 3b, as these panel labels are not referenced anywhere in the manuscript.

      We have carefully edited, and improved, figures throughout the text and hope that they are now acceptable.

      (2) Please change '1D' to '2D' in Figure S2.

      This has now been done and we apologise for this carelessness.

    1. eLife Assessment

      This is a useful and clearly presented study showing how simple feedback between cell crowding and cell growth can maintain tissue balance and lead to the spontaneous formation of stem-cell clusters. The evidence supporting the behavior of the model is solid, but the significance of the clustering result would be strengthened by relating the emphasizing the differences to prior studies, and potentially relating the clustering mechanism to properties of critical birth-death processes in two dimensions.

    2. Reviewer #1 (Public review):

      Summary:

      This theoretical study of a simple system of stem, transient progenitors and fully differentiated cells provides interesting results about how tissues can reach homeostasis with different proportions of these cell types. The key ingredient is the feedback due to pressure, which regulates the proliferation rate. The study shows that by varying the rates at which stem cells switch to progenitor cells, and progenitor cells to differentiated cells, the final composition of the tissue can be tuned. In addition, the model shows that clusters of stem cells can arise in this model due to their effective immobilization and therefore that stem-cell niches can arise even in the absence of specific adhesion between different cell types.

      The model is calculated using both cell-based simulations and continuum dynamics, and is thoroughly analyzed in its spatial and temporal characteristics.

      Strengths:

      The model is simple, yet leads to very rich results. The authors demonstrate the results of the model using both particle-based simulations and a continuum model, thereby enhancing our understanding of this complex system.

      Weaknesses:

      The origin of the stem cell niche is not so clearly explained.

    3. Reviewer #2 (Public review):

      Summary:

      This is a theory/computational paper that explores a model of a two-dimensional tissue undergoing homeostatic turnover. The motivation is to explore how the rates of cell division and cell loss in a tissue can be precisely balanced to maintain tissue integrity. It is already well appreciated that this can be achieved by a feedback mechanism whereby cells tune their division or loss rate in response to a change in cell density relative to a homeostatic set point (e.g. PMID 30269903 and other papers cited in the introduction to this new manuscript). Cell density can be sensed locally by mechanical pressure.

      Here, the question asked is whether pressure-dependent homeostatic control can maintain not only cell density (as previously known) but also cell type proportions when a tissue has a proliferative hierarchy. The specific example considered is a 3-tier hierarchy with stem cells (S) that give rise to transit-amplifying cells (T) that give rise to terminally differentiated cells (D), which are then lost. Next, the authors notice in simulations that in such a model of density-dependent cell division, stem cells undergo spontaneous clustering without any need for cell adhesion. Thus, the key result of the paper is that a simple pressure-based homeostatic feedback control leads to spontaneous formation of stem cell clusters, which the authors call "niche-like". They characterize these clusters numerically.

      Overall, the work is elegant and presented in a pedagogical manner. But I think its central result has already been previously described (DOI 10.1103/PhysRevE.77.031907), albeit without the use of terms such as "niche-like" that increase the relevance of the work, and using a different detailed choice of model. Essentially, clustering is the outcome of a log-divergence of the density of random walkers undergoing a critical birth-death process in two dimensions, as discussed in the 2008 paper. Essentially, once stem cell division is coupled to total density (rather than just stem cell density), then the stem cells follow a critical birth-death process in 2D, and the clustering is an inevitable outcome. This is the precise point of the previous paper. As such, my review will focus on novelty compared to prior work.

      Strengths:

      The first strength of the paper is that it gives a clear and biologically oriented presentation of how pressure-dependent (or more simply, density-dependent) feedback can be sufficient to maintain steady-state homeostasis with defined proportions of stem, transit-amplifying, and differentiated cells. The authors scan parameters in numerical simulations using an established agent-based model of cells under mechanical tension, and show that tissue can be maintained provided that stem cell division rates are able to match loss rates. This is not very surprising, but I believe it has not been laid out before in this way, and it is clearly presented.

      A second strength is that the paper may make a useful contribution by drawing attention to the connection between mechanical homeostatic feedback and the spatial organization of stem/progenitor cells. Even though there is a precedent for this point (see overview above), the work is more biologically oriented than the prior paper, and may therefore be useful to readers interested in how simple local rules can shape tissue organization. The current paper uses a more biophysically informed particle-based model for simulations than the prior work, and it provides a useful framework for studying the interaction between mechanical feedback, tissue homeostasis, and cell-type hierarchy that could be generalized. The simulations are well motivated, and the results are presented in a pedagogical manner. More on the comparison to the 2008 paper below.

      Weaknesses (or rather, comparison to prior work):

      The main weakness is that the central result of the paper appears to have already been previously described, albeit using different terminology and a different detailed choice of model. I will describe the similarities and differences between the two papers below.

      The 2008 paper...

      • ...is motivated by asking how a critical birth-death process can maintain uniform density in 2D. This is different from the current paper, which asks whether a birth-death process can be tuned by total density and still maintain cell type proportions. As such, the questions motivating the work are subtly different, although both worry about how homeostasis is maintained.

      • ...considers a 2-tier model consisting of progenitor and differentiated cells (A->B->0). This is different from the current paper, which considers a 3-tier model (S->T->D->0). But the current paper is aiming to be general rather than specific, and I would propose that complexifying the model from a 2-tier hierarchy to a 3-tier hierarchy doesn't make the new paper qualitatively different.

      • ...shows that "the population of progenitor cells should cluster over time" (quoted from the paper). This is shown for A cells in the 2008 paper, and is shown for stem (S) cells in this new paper. As such, the prior paper already shows the key result of this new paper.

      • ...uses slightly different rules, which are clunkier: it allows for symmetric and asymmetric division of A cells, does not allow direct A cell differentiation (no A->B allowed), and it uses a hard-core lattice-based model with hole-diffusion to model density-dependent cell division. By contrast, the current paper uses a more biophysically informed two-particle agent-based model for simulations, and it only considers symmetric divisions and direct differentiation. As such, the current paper is a more realistic treatment than a lattice-based model, and simply different (not more or less realistic) in terms of ignoring epigenetic heritable sister cell correlations. But since clustering is a large-scale phenomenon, as is often the case in statistical physics, the microscopic implementations should not be important. As such, I would not expect the difference in rules to be important to the clustering.

      • ...derives a long-term asymptotic form for the clustering dynamics of progenitor cells (Equation 15 of that paper, and the unnumbered equation at the top of column 2 of page 7 of the paper). This goes beyond the new paper, which only describes the phenomenon numerically. The 2008 paper also explains the phenomenon in terms of prior work on random walkers undergoing birth-death processes, which better links the phenomenon to prior statistical literature.

      • ...shows experimental evidence for clustering of progenitors in mouse epidermis. This goes substantially beyond the current paper, which is entirely computational. That said, despite the excellent fit of data to the theory, I am myself not convinced that the clustering seen in epidermis is the result of this phenomenon alone.

      • ...studies clonal dynamics and shows that it is consistent with a voter-model-like behavior, with clone size growing as t/log(t). The scaling law is different from that reported in the current paper.<br /> Thus, the key result that progenitor cells undergo clustering seems to have been mathematically described in the 2008 paper and even shown in that paper to be consistent with experimental data in one tissue.

      That said, I do think that this new paper may make a useful contribution. It is certainly more biologically oriented (despite lacking experimental data, which the prior paper has). And it may be that the quantitative dynamics of clustering and clonal competition are subtly different in this model. So, with certain revisions, I am supportive of this work.

      Specific suggestions:

      (1) The new paper should appropriately cite precedent. The paper should avoid making claims that progenitor clustering in this model of birth-death with spatial density control is a major novel discovery. This was a key result of the previous paper. The new paper should also cite the 2008 paper for evidence that clustering of this type has been proposed to be seen experimentally. The differences between the papers discussed above could be used as needed in highlighting the contribution of the new work.

      (2) There is still a question about whether microscopic details of the simulation may alter the asymptotic dynamics of the clusters and clones. The authors could compare the dynamics of clustering between their simulations and the 2008 lattice-based result (Equation 15 of that paper, the "1/log(t)" behavior of the density radial correlation function) derived in the original paper, and similarly compare clonal dynamics in that paper to this new paper. Whatever they find is interesting: if they confirm that the same dynamic behaviours occur, it will reinforce the results of the prior work, rephrase the problem in more relevant terms, and help make it accessible to a broader audience. If they show that their model leads to different quantitative dynamics, they could explore (in a supplement perhaps?) what is the origin of the difference, given that the models are superficially similar.

    4. Reviewer #3 (Public review):

      Summary:

      The manuscript studies a minimal model of tissue homeostasis in which stem (S), progenitor/transit-amplifying (T), and differentiated (D) cells interact through stochastic fate decisions and mechanically regulated proliferation. Combining a compartment-level analytical description with particle-based simulations, the authors show that pressure-dependent growth can stabilise cell populations and maintain tissue homeostasis over a broad parameter range. They derive homeostatic cell fractions and identify distinct regimes, including tissues maintained by rare stem cells or by long-lived progenitors. Most notably, the simulations reveal the spontaneous emergence of persistent stem-cell clusters and renewal-unit-like structures in the absence of explicit niche signals or differential adhesion. The authors further analyse spatial correlations, cluster statistics, lineage dynamics and neighbourhood compositions, and argue that stochastic fate decisions qualitatively alter the spatial organisation of mechanically regulated tissues. Based on the annotated manuscript, several of these central claims concern the emergence and stability of stem-cell clusters and their interpretation as self-organised structures.

      Strengths:

      The manuscript is clearly written, technically competent, and combines analytical calculations with large-scale agent-based simulations in an elegant way. The systematic analysis of spatial observables, including pair correlations, cluster-size distributions, void statistics, neighbourhood composition, and lineage tracing, provides a rich characterisation of tissue organisation. The comparison with the authors' previous work on deterministic stem-cell hierarchies is particularly interesting, as it demonstrates that altering the fate-choice scheme can qualitatively change the resulting tissue architecture from stem-cell repulsion to stem-cell clustering. The study therefore generates a number of experimentally testable predictions regarding stem-cell spatial organisation and lineage dynamics. I also found the attempt to connect microscopic fate decisions to mesoscopic tissue-level organisation valuable, as this remains a relatively underexplored area at the interface of stem-cell biology and tissue mechanics.

      Weaknesses:

      My main concern relates to novelty and positioning within the existing literature. The manuscript frequently presents mechanical feedback as a mechanism capable of generating tissue homeostasis and robust cell-type proportions. However, the idea that crowding- or pressure-dependent feedback can robustly stabilise renewing tissues is already well established, both in mechanical homeostasis models and in more general feedback-based theories of tissue regulation. Consequently, the homeostasis aspect of the present manuscript appears more as an application of existing principles to a particular mechanical framework than as a fundamentally new conceptual advance. The authors should position their work more clearly with respect to these studies and explain more explicitly what genuinely new biological or mathematical insights are provided beyond the existence of homeostatic steady states.

      The manuscript therefore derives its novelty primarily from the spatial phenomena emerging from the coupling of stochastic fate decisions and mechanical interactions. While the observed stem-cell clustering is intriguing, the mechanistic explanation remains incomplete. The authors attribute clustering to stochastic symmetric divisions combined with mechanical control, yet it remains unclear whether the observed structures genuinely arise from a new self-organised mechanical mechanism or simply reflect local genealogical correlations generated by repeated symmetric divisions. Several of the strongest conclusions, including long-range order, renewal-unit formation, and stable niche-like organisation, require more rigorous support. In particular, the manuscript currently lacks appropriate control models that would disentangle the effects of mechanics, stochasticity, and daughter-cell placement. Furthermore, the definition of clusters via a specific distance threshold appears somewhat arbitrary and may itself influence the inferred cluster sizes and conclusions regarding finite renewal units. It would be important to demonstrate robustness with respect to this choice.

      I also have concerns regarding the simulation methodology and interpretation of the spatial statistics. Large tissues are assembled from smaller pre-equilibrated patches. While this may be computationally convenient, it raises concerns about whether long-range structures can emerge naturally and whether the reported spatial organisation could be affected by the construction procedure. Given that one of the central claims concerns spatial ordering over large scales, it would be important to demonstrate explicitly that the patching procedure does not influence the results. Similarly, the manuscript interprets pair-correlation and void statistics as evidence for long-range ordering of stem-cell clusters, but does not sufficiently exclude alternative explanations arising from finite system size, patch construction, or simple genealogical clustering.

      Conclusion:

      Overall, I find the manuscript interesting, whilst I also believe it should have a major revision. The most valuable contribution of the study is not the demonstration that mechanical feedback can maintain homeostasis, but rather the suggestion that stochastic fate decisions can qualitatively reshape the spatial organisation produced by mechanically regulated tissues. For a broad-interest journal, however, the authors should sharpen the positioning of the work relative to prior homeostasis theory, establish more clearly the mechanistic origin of the reported stem-cell clustering, and provide additional evidence that the observed structures are genuine emergent features of the model rather than artefacts of modelling assumptions or analysis choices.

    5. Author response:

      Thank you for providing detailed and positive feedback. This will help us to improve manuscript and its accessibility for a broad audience.

      We first want to highlight what we believe is the main aspect, and therefore novelty, of our work: Combining cell-cell mechanics, mechanical regulation of growth and stochastic cell fate decisions leads to a stable population of stem cells in a stable balance with more differentiated cells, avoiding neutral drift, extinction or overgrowth. Furthermore, this combination leads to stable clusters, i.e. without further coarsening. We call these structures “niche-like”, as we believe stability is fundamental for biological function.

      As the referees point out, this work builds on previous works in the literature, and highlighting the differences and advances will benefit the manuscript. As referee 3 points out, homeostatic regulation by mechanics, in particular pressure, has been discussed intensively in the literature, by us and many others. The novelty in our work is that we apply this regulatory mechanism to a multi-component tissue, and there can stabilize not only the total number of cells, but also their relative composition. In particular, we derive analytical equations relating composition to differentiation rates, and demonstrate their validity in agent-based simulations. In the revised manuscript we will highlight more clearly that this is an application of previous works on a novel system.

      Referee 2 points to Klein et al. (2008). We do agree that their model also shows clustering, and therefore that we should have compared and contrasted our model more thoroughly with this work. However, as we will show more clearly in a revised manuscript, our results differ crucially from this form of clustering without detailed mechanical interactions. As also discussed in the original work by Klein et al., the mechanism therein results in clusters which are not stable, but continue to grow indefinitely over time (although at a slow rate). We show that our simulations display stable clusters which do not grow further, even when simulating for very long time scales, and that these clusters are well-defined, by structurally different correlation functions showing clear inter-cluster separation. In the revision, we will demonstrate that even when starting from a perfect cluster, the stem cells separate, resulting in finite stable clustering. Thus, our clusters are true “fix points” of the stem cell dynamics. Referee 3 rightfully recommends that we should identify more clearly which mechanism is responsible for the stable niche-like structures. To this end, we will compare more closely to Klein2008 as a model without mechanics leading to log(t) coarsening, and our earlier work (Krämer et al., 2024) where stem cells were immortal. There, we had found that mechanics alone produces the reverse result, i.e. leads to enhanced separation of stem cells, which anti-cluster and are dispersed as single cells. We will add new simulations as an additional control that contain cell-cell mechanics, but no mechanical regulation of growth. This combination will allow us to disentangle how the combination of cell mechanics, mechanical feedback on growth and stochastic differentiation results in the formation of stable niche-like structures.

      Note that neither Klein2008 nor Krämer2024 have a physical mechanism that maintains the overall stem cell population number stable. Krämer2024 simply fixes the number of stem cells, whereas the model in Klein 2008 results in stochastic drift in stem cell number.

      Evidentially, we will also address the more technical weaknesses of the manuscript identified by the referees. In particular, we will demonstrate more clearly that the observed structures are genuine emergent features of the model rather than artefacts of modelling assumptions or analysis choices, especially with respect to the patching technique used to obtain large simulations.

    1. eLife Assessment

      This valuable study investigates the role of dinucleoside polyphosphates in regulating the cGAS-STING-TBK1 axis of innate immunity. The data collected using multiple biochemical and cellular assays, plus X-ray crystallography, are solid, but testing additional polyphosphates and using NUDT2KO cells will further strengthen the main conclusion. This paper will be broadly relevant to those studying various innate immune pathways.

    2. Reviewer #1 (Public review):

      Summary:

      The manuscript entitled "NUDT2 loss defines quantitative limits for dinucleoside polyphosphate action on the cGAS-STING-TBK1 axis" investigates whether intracellular levels of dinucleoside polyphosphates can influence signaling through the cGAS-STING pathway. The study was motivated in part by a previous report proposing that Ap4A can bind to STING and inhibit its activity.

      In the present work, the authors prepared recombinant human and mouse cGAS, STING, and TBK1 and systematically investigated their interactions with Ap3A, Ap4A, and Ap4G, which represent biologically relevant dinucleoside polyphosphates. To characterize these interactions, they employed a broad range of biochemical and structural approaches, including isothermal titration calorimetry (ITC), HPLC analysis of enzymatic reactions, X-ray crystallography, and a time-resolved fluorescence kinase assay.

      First, the authors prepared human and mouse STING in both wild-type form and as specific mutants previously associated with altered binding of the canonical STING ligand cGAMP. ITC measurements showed that, whereas cGAMP binds STING with nanomolar affinity, none of the tested dinucleoside polyphosphates displayed detectable binding to any of the STING variants. The authors then investigated binding of ATP, GTP, and the dinucleoside polyphosphates to a mutant form of cGAS by ITC. Interestingly, all three dinucleoside polyphosphates showed apparent binding in a range similar to that of the tested NTPs. However, subsequent structural analysis did not reveal electron density corresponding to the dinucleoside polyphosphates in the active site.

      The authors further examined the effect of Ap4A on TBK1 and found that Ap4A can inhibit TBK1 activity, but only at very high concentrations. Finally, they quantified intracellular ATP, GTP, Ap3A, Ap4A, and Ap4G levels in NUDT2-knockout cells. Although Ap4A and Ap4G concentrations increased substantially upon NUDT2 deletion, the measured intracellular concentrations generally remained below those required to significantly affect components of the cGAS-STING-TBK1 pathway in vitro.

      Overall, the manuscript is clearly written and represents a careful and rigorous piece of biochemical work. I particularly appreciate the authors' effort to critically examine and clarify conclusions from previous studies. The experimental work is comprehensive.

      Strengths:

      A major strength of the study is the breadth and rigor of the experimental strategy. The authors combine binding measurements, enzymatic assays, structural analysis, kinase assays, and direct measurements of intracellular metabolite concentrations. Their data provide convincing evidence that the tested dinucleoside polyphosphates do not bind STING with physiologically relevant affinity and that inhibition of TBK1 by Ap4A occurs only at concentrations substantially higher than those normally reached in cells. Importantly, the authors also quantify the intracellular concentrations of these metabolites in cells lacking NUDT2 and show that, although Ap4A and Ap4G levels increase significantly, they generally remain below the concentrations required to affect the tested signaling proteins in vitro. This quantitative comparison between biochemical effects and intracellular metabolite levels is particularly valuable and strengthens the physiological interpretation of the study.

      Weaknesses:

      Interpretation of the pppGpA density in the cGAS structure.

      Importantly, when Ap4G was used in the crystallographic experiments with cGAS, the authors observed density corresponding only to pppGpA. They interpret this observation as potentially resulting from processing of Ap4G by DNA-bound cGAS to generate a canonical reaction intermediate.

      Based on my direct experience with structural studies of an unrelated enzyme using commercially supplied Ap4G from Jena Bioscience, I believe that an alternative explanation should be carefully considered. In our experience, Ap4G preparations can contain small amounts of pppGpA, likely at the per mille level, as a side product of the chemical synthesis. If the studied protein has substantially higher affinity for pppGpA than for Ap4G, even such a minor contaminant can become strongly enriched in the active site during crystallization and consequently dominate the observed electron density.

      Indeed, traces of a corresponding contaminant may already be visible in the HPLC profile presented in Figure S2.3C. I therefore strongly suggest repurifying Ap4G by HPLC and repeating, at minimum, the HPLC-based experiments shown in Figure S2.3C using the purified material. Ideally, the purity of the Ap4G preparation used for crystallography should also be carefully documented.

      Until this possibility has been excluded experimentally, I would recommend that the authors substantially temper the conclusion that pppGpA observed in the crystal structure originates from cGAS-mediated processing of Ap4G.

    3. Reviewer #2 (Public review):

      Summary:

      Dinucleoside polyphosphates have been found to increase in responses to cellular stress and are involved in the regulation of transcription, antibiotic resistance, DNA replication and innate immunity in metazoa. Increased Ap4A levels have been previously found to regulate the cGAS/STING pathway, leading to the proposal that modulation of Ap4A levels may be a suitable pathway to pharmacologically modulate cGAS/STING signalling (Guerra et al., 2020). Here, Weiland et al. present compelling biochemical, biophysical and structural evidence that dinucleoside polyphosphates (Ap3/4A and Ap4G) do not interact with STING nor are likely to affect cGAS signalling in vivo.

      The evidence that they present evaluates the interactions of Ap3/4A and Ap4G s with cGAS, STING and TBK1 kinase as key signalling molecules in the cGAS/STING innate immune response. Previous work has identified Ap4A as a regulator of cGAS/STING signalling, and through precise characterisation of the biophysical interaction, crystallographic as well as modelling data provide evidence that these effects are too weak to affect these processes in vivo. Their work identifies that there are no direct interactions between STING and dinucleoside polyphosphates at physiological concentrations and at hyperactivated levels in NUDT2 mutants. There are detectable interactions of both Ap4A/and Ap4G with cGAS. In the presence of DNA, cGAS shows either ATP when bound with Ap4A or an intermediate of the cGAMP synthesis when crystallised with Ap4G. This is an interesting observation, showing Ap4G appears to serve as a weak substrate for canonical cGAMP production in the presence of DNA. Finally, the effect of Ap4A on the catalytic activity of TBK1 was evaluated and showed very weak effects. Competition assays in the presence of ATP and Ap4A/Ap4G showed a higher inhibitory effect for Ap4G, but only at concentrations 5x or greater than intracellular ATP levels and much weaker effects for Ap4A (~32x ATP levels).

      Strengths:

      The approaches used provide robust data for a subset of physiological Ap4Ns. The strength of their evidence comes from the complementary structural and biophysical approaches investigating the interactions of cGAS, STING and TBK1 independently. These data, including the dissociation constants for Ap4A and Ap4G with cGAS, allow for comparison with physiological levels of the dinucleoside polyphosphate in vivo. The use of quantitative mass spectrometry provides some increased rigor to the conclusions by integrating the in vitro and in vivo studies.

      Weaknesses:

      The data presented for Ap4G and Ap4A are convincing, and conclusions are consistent with the evidence presented. However, there are other Ap4Ns (Ap4C/U) that may be active against cGAS/STING/TBK1 that are not included in this study, which weakens the conclusions. This could be rectified relatively simply. The data would be more robust if the effects of Ap4C and Ap4U were included for cGAS using ITC, and also the additive effect of competition assays with physiological ATP and the levels of all Ap4Ns in the same reaction as in Figure 3A. At present, each Ap4N is investigated in isolation. To determine the effect of physiological Ap4N levels, reactions with all 4 Ap4Ns at maximum physiological levels as determined from NUDT2 KO cells would provide a robust test of their activities.

      Overall, the in vivo data suggest that Ap4A and Ap4G can reduce cGAS activity, but only weakly and only at levels that are unachievable even where NUDT2 is inactivated. The data suggest that Ap4A/Ap4G do not affect cGAS/STING/TBK1 signalling at physiological levels and that NUDT2 targeting would be ineffective for the pharmacological modulation of cGAS/STING/TBK1 signalling. This conclusion is accurate only if Ap4C/U do not contribute to this inhibition. At present in the absence of these data, this conclusion cannot be made, as their activity could in principle affect cGAS/STING signaling.

    4. Reviewer #3 (Public review):

      Recent work reported that STING binds Ap4A and proposed that elevated Ap4A levels could modulate the cGAS-STING pathway. Intracellular Ap4A levels are controlled by the hydrolase NUDT2, itself a cancer drug target, and loss of NUDT2 raises cellular Ap4A pools. Building on this, the authors set out to investigate whether and how Ap4A modulates the cGAS-STING pathway. This question was motivated by the fact that the mechanism by which STING might bind a linear dinucleotide such as Ap4A remained unclear, and by the existence of alternative routes of crosstalk between Ap4A and the pathway. These include the chemical similarity between Ap4Ns and pppGpA, the linear intermediate generated by cGAS during cGAMP synthesis, raising the possibility that Ap4A could inhibit cGAS directly, as well as a potential downstream effect on the kinase TBK1.

      Using in vitro biochemistry, the authors show that STING does not, in fact, bind Ap4A, contrary to what was previously claimed, and they further examine the impact of NUDT2 loss on the cGAS-STING pathway. This clarifies for the field whether Ap4A could influence the pathway in any way, whether through binding to cGAS, STING, or TBK1.

      The authors find that cGAS can bind Ap4A but cannot process it. cGAS also binds Ap4G, but processes it only inefficiently and only when supplied at very high concentrations in vitro. This argues against physiological relevance in cells, since inhibition of canonical cGAMP synthesis would require Ap4Ns to be present in excess over ATP and GTP, a condition unlikely to be met at physiological nucleotide concentrations even when NUDT2 is absent. The authors also test for effects on TBK1, showing that while it can bind Ap4A, the concentrations required for inhibition are similarly not physiological.

      The authors conduct a thorough series of biochemical and biophysical experiments to clarify the underlying mechanism, systematically ruling out a plausible effect of Ap4A in cells at each step. Even where an effect on cGAS or TBK1 can be detected at very high, non-physiological concentrations, this remains confined to in vitro conditions. The experiments are sound and conducted with scientific rigor, and the results support the authors' conclusions.

      This work provides clear evidence that STING does not bind Ap4Ns, and that even where binding does occur elsewhere in the pathway, there is no plausible mechanism by which Ap4Ns could modulate the cGAS-STING pathway under physiological conditions. This resolves an open question for the field: if Ap4Ns do have biological effects related to this pathway, these are unlikely to arise from a direct interaction with cGAS, STING, or TBK1, and must instead be mediated through other processes. The methods and data will be a useful reference for the community when evaluating future claims of nucleotide second-messenger crosstalk.

      Prior studies have shown that NUDT2 disruption, which elevates cellular Ap4A levels, is associated with down-regulation of interferon-response pathways. This is notable in light of the current findings, which show no direct biochemical interaction between Ap4A and cGAS, STING, or TBK1. Together, this suggests that if elevated Ap4A does suppress IFN production, it likely does so indirectly, through mechanisms other than direct engagement of the cGAS-STING-TBK1 axis.

  2. Oct 2026
    1. eLife Assessment

      This valuable study tests to what extent dopamine neuron stimulation produces striatal serotonin release, and vice versa - a topic of interest to neuroscientists studying the basal ganglia, neuromodulation, and reward learning. Employing a combination of modern optical and genetic methods, it provides solid evidence that the mesolimbic dopamine and serotonin systems operate primarily in parallel, with little crosstalk observed when each of the pathways are activated individually. The results are generally convincing, although the reviewers were still concerned about the absence of control experiments examining light artifacts.

    2. Reviewer #1 (Public review):

      In this study, Chaudun et al use optogenetics and genetically encoded neuromodulator sensors to test to what extent dopamine neuron stimulation produces striatal serotonin release, and vice versa. The study is timely given growing interest in dopamine/serotonin interactions, but important controls are missing. The authors find that striatal dopamine and serotonin afferents function largely independently, with dopamine neuron stimulation producing no striatal serotonin release and serotonin neuron stimulation (possibly) producing minimal striatal dopamine release.

      In the revised manuscript draft, the authors increased sample sizes for some important experiments, removed inconclusive electrophysiology data, reorganized the figures, and added new anatomical tracing experiments. The critical fluorophore-only controls for the optostimulation experiments requested by reviewers were not done.

      Unfortunately, with the exception of increased samples sizes, these revisions have done little to substantially improve the strength of the evidence behind the authors' conclusions. These conclusions, as reflected in the new title of the paper, have also slightly changed in a manner that puts them further at odds with the data presented. As a result, my opinion continues to be that the study is interesting and the experiments are useful, but important controls are missing, a more cautious interpretation is warranted, and the organization of the study is confusing. I explain my reasoning below.

      (1) A major criticism of the original manuscript was that the electrophysiology experiments were only tangentially related to the question under study. The manuscript has now been reorganized in such a way that the cocaine experiments are similarly irrelevant. In the authors' own words, the central question of the study is "does activity in one [DA or 5-HT] system directly recruit transmitter release from the other, or do DA and 5-HT operate mainly through parallel circuits?". Optogenetically stimulating release of dopamine while recording serotonin activity (or vice versa) begins to answer this question, and in the original draft, cocaine-evoked dopamine and serotonin recordings were presented side by side with the opto-evoked dopamine/serotonin data. In this configuration, the drug-evoked recording data allowed readers to compare the magnitude of dopamine/serotonin release evoked by an artificial optogenetic manipulation to that evoked by a behaviorally relevant pharmacological treatment. This serves a purpose. In the revised manuscript, the cocaine experiments are presented in a standalone first figure. As the authors themselves note in their response to reviewer 2, cocaine independently evokes dopamine and serotonin accumulation is therefore poorly suited on its own to test how activating one neuromodulatory system affects the other. Thus, the cocaine data presented alone in this way neither address the paper's central question nor support the paper's conclusions.

      (2 The original manuscript suggested that one way by which striatal serotonin activity could recruit striatal dopamine release would be if the same serotonin neurons project to both VTA and striatum. To test this, the authors use retrobeads to identify DS-, NAc-, and VTA- projecting dorsal raphe neurons. However, they neither identify whether the labeled neurons are serotonergic nor evaluate colocalization between the retrograde tracers as would be required to ascertain whether the same serotonin send axon collaterals to more than one region. Thus, the anatomical data presented do not answer the question of whether the same serotonin neurons innervate both targets.

      (3) As noted in the original round of review, applying light stimulation to mice expressing a fluorophore-only construct without opsin is an important control in optostimulation experiments that is widely standard in the field. The omission of such a control is a weakness of the paper. The authors suggest that other results presented - such as the lack of green serotonin sensor activation when red light is applied in the midbrain in Fig 2G-H - argues against cross-talk between the red and green optical channels. This is technically correct, but ruling out cross-talk between optical channels is not the only reason such a control is necessary. The high intensity red light used for optostim travels several millimeters through tissue and can reach the retina. Although mice do not have cones to detect red light (and therefore cannot distinguish the color red from other colors), their rods are sensitive to red light. As a result, bright red intracranial optostim can be perceived by the animal as a flashing light, and this sensory stimulus can produce a neural response. The absence of such a response in the serotonin system in Fig 2G-H does not rule out the possibility that the small response in the dopamine system observed in Fig 3G-H is driven by sensory input rather than a bona fide physiological effect of serotonin neuron activation on striatal dopamine release. Crucially, sensory responses to the stimulating light would also show 2Hz entrainment to the frequency of the stimulating laser trains. In other words, without a fluorophore-only control, the entirety of the authors' results pertaining to serotonin-evoked dopamine release can be explained by artifactual effects of the optostimulation. If this opsin-negative control is absolutely impossible for the authors to perform, at the very least this major limitation should be clearly and explicitly addressed in the discussion because it pertains directly to the study's main conclusions. This brings me to my final point.

      (4) Taken together, the authors' data appear to support two primary conclusions. First, optogenetic dopamine stimulation has no effect on striatal serotonin release. Second, synchronous and supra-physiological activation of the serotonin system has a very modest effect on striatal dopamine release that might be entirely explained by artifacts of the optostimulation protocol. The miniscule magnitude of the evoked dopamine response observed following maximally strong (15-20 mW) optogenetic serotonin stimulation also bears consideration here. It argues that even if the observed dopamine release effect is not an artifact of the optostimulation protocol, serotonin-evoked dopamine release is likely to be vanishingly small or absent in other conditions when serotonin neuron firing is neither supraphysiological nor synchronous. The new title strongly implies the existence of significant cross-talk, which is not well supported by the data. Claims throughout the text that call the cross-talk effect reliable or otherwise robust are similarly overstated.

    3. Reviewer #3 (Public review):

      The authors suggest that the small release of DA may be due to a release of glutamate from DRN 5-HT neurons to the VTA that stimulates weakly and in a transient fashion the VTA DA neurons, which in the end, produce a transient and small release of DA in the NAc.

      Their findings give more information on the previously reported complex and partial known crosstalk between 5-HT and DA in the NAc.

      Comments on revised version.

      The authors have added several new experiments that address some of the prior concerns. However, there are still points of confusion. For example, the authors repeatedly argue that 5HT or DA are synaptic when in fact most of the transduction mechanisms for these neuromodulators and their transporters are extra synaptic, as indicative of volume transmission. This is not well articulated in the authors' interpretative scheme.

    4. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment:

      This useful study employs optogenetics, genetically-encoded dopamine and serotonin sensors, and patch-clamp electrophysiology to investigate modulations of neurotransmitter release between striatal dopamine and serotonin neurons - a topic of interest to neuroscientists studying the basal ganglia. The results suggest that the dopamine and serotonin systems operate largely in parallel, with the activation of serotonin neurons resulting in a small, transient dopamine release. The authors suggest that this interaction occurs via glutamate release in the ventral tegmental area, findings that are closely related to previous work. Some conclusions are incomplete requiring larger samples-sizes and controls.

      We sincerely thank the editor and reviewers for their thoughtful and constructive comments. Addressing their concerns required extensive additional experimental work and the involvement of new personnel. We increased sample sizes, completed previously missing experimental groups, generated a largely new dataset, and performed additional anatomical experiments and analyses. We also removed the patch-clamp electrophysiology experiments to sharpen the study’s focus and extensively revised and reorganized the manuscript to address methodological and interpretative inconsistencies. Although the central concept remains unchanged, the revised manuscript provides a substantially more rigorous and consistent account of the limited and directionally asymmetric circuit-level coupling between striatal dopamine and serotonin release.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In this study, Liu et al use optogenetics and genetically encoded neuromodulator sensors to test the extent to which dopamine neuron stimulation produces striatal serotonin release, and vice versa. The study is timely given growing interest in dopamine/serotonin interactions and in the context of recent work showing bidirectional and dynamic regulation of striatal dopamine by another neuromodulator, acetylcholine. The authors find that striatal dopamine and serotonin afferents function largely independently, with dopamine neuron stimulation producing no striatal serotonin release and serotonin neuron stimulation producing minimal striatal dopamine release. This work will inform future work seeking to dissect the contributions of striatal dopamine, serotonin, and their interactions to various motivated behaviors. While the paper's main conclusions are adequately supported (see Strengths), additional controls and experiments would significantly broaden the paper's impact (see Weaknesses). Finally, this draft of the work is poorly presented with numerous errors, omissions, and inconsistencies evident throughout the text and the figures that should be addressed.

      Strengths:

      The study employs optogenetic stimulation simultaneously with fiber photometry recording of dopamine or serotonin release measured with genetically encoded sensors. These methods are state-of-the-art, offering tighter temporal control compared to pharmacological methods for manipulating dopamine and serotonin and improved selectivity over techniques like electrochemistry and microdialysis used to record neuromodulator release in previous studies on the subject. As a result, the paper's main conclusions are well supported.

      Weaknesses:

      (1) The electrophysiology experiments in Figure 3 are only tangentially related to the focus of the study, and their findings are almost entirely irrelevant to the paper's main conclusions. The results of these experiments are also not novel. Glutamate corelease from 5-HT neurons has been previously shown, including in the OFC and VTA (Ren et al, 2018, Cell, McDevitt et al, 2014, Cell Rep, Liu et al 2014, Neuron; and others). The authors should explain more clearly what they think these data add to the manuscript and/or consider removing them altogether.

      We thank the reviewer for their insightful comments regarding the patch-clamp electrophysiology data. We acknowledge the concerns raised by multiple reviewers and have therefore removed these data from the revised manuscript to improve clarity and focus.

      (2) Related to the point above, as far as I can tell, the only value the electrophysiology data add is to suggest that perhaps activation of serotonin neurons may drive minimal striatal dopamine release via glutamate corelease in the VTA. The evidence provided in this version of the manuscript is insufficient to support that claim, but the manuscript would be significantly strengthened if the authors tested this hypothesis more directly. One way to do that could be to stimulate serotonin axons in the striatum (as opposed to the serotonin cell bodies) and record striatal dopamine release.

      This is an interesting approach. However, given the divergent projection of serotonin fibers and the need for a strict spectral separation between excitatory opsin and transmitter sensor fluorescence, we retained the current approach, which allows us to investigate circuit-level interactions while minimizing technical confounds.

      A complementary anatomical approach would be to use retrograde tracing to test whether the DR 5-HT neurons projecting to the striatum are the same or different from the VTA projecting population.

      We performed the suggested experiment and have included the new data in the revised manuscript (Figure 1F–H). This approach revealed a clear topographical organization. These results indicate that the identified neuronal populations are largely segregated.

      (3) The findings would be strengthened by the addition of a fluorophore-only control group lacking opsin expression in all experiments in Figures 1 and 2.

      We thank the reviewer for this important suggestion. We requested authorization to add an opsin-negative control cohort, but this request was not approved under the applicable animal-welfare regulations. We therefore cannot fully exclude nonspecific physiological effects of light delivery and now acknowledge this limitation in the Discussion. Nevertheless, the stimulation-locked response was confined to the biosensor channel and absent from the isosbestic channel, arguing against direct optical contamination. Moreover, application of the same optical stimulation protocol in the VTA did not generate a detectable stimulation-locked 5-HT signal. Although these observations do not fully replace an opsin-negative control, they support the interpretation that the response reflects a biological signal rather than direct optical interference.

      (4) The experiment of stimulating serotonin neurons and recording serotonin release in the NAc was not performed. It would be useful to be able to compare the magnitudes of evoked serotonin release in these two striatal regions, though it is not central to the main claims of the paper.

      This point has now been addressed with additional experiments, and the figure has been reformatted accordingly in the revised manuscript.

      (5) The interpretation of the results from Figure 2 is described inconsistently throughout the manuscript. The title implies there is significant crosstalk between the dopamine and serotonin systems. The abstract calls the crosstalk "transient", which is a description of its temporal dynamics, not its magnitude. Then the introduction figures and discussion all suggest the crosstalk is minimal. I suggest the authors describe the main findings - minimal crosstalk between the dopamine and serotonin systems - clearly and consistently in the title, abstract, and main text.

      We agree with the reviewer and have revised the title, abstract, Results, figure legends, and Discussion to describe the magnitude and directionality of the interaction consistently. The revised manuscript now emphasizes that striatal dopamine and serotonin release operate largely in parallel, with limited and asymmetric crosstalk: activation of VTA dopamine neurons did not produce detectable serotonin release, whereas activation of dorsal raphe serotonergic neurons induced only a weak, stimulation-locked dopamine signal without a sustained increase in dopamine amplitude. The revised title, “Asymmetric Circuit-Level Crosstalk Between Dopamine and Serotonin Release in the Striatum,” and the corresponding changes throughout the manuscript now more accurately reflect these findings.

      Reviewer #2 (Public review):

      Summary:

      This brief communication aims to clarify interactions between the dopamine (DA) and serotonin (5-HT) systems of mice. The authors use optogenetic stimulation of DA neurons in the VTA or of 5-HT neurons in the DRN, while monitoring the fluorescence of DA and 5-HT sensors in the nucleus accumbens (NAc) and dorsal striatum (DS) using fiber photometry. The authors report on a small release of DA in the NAc following DRN stimulation, which they attribute to glutamate co-release onto VTA DA neurons using slice electrophysiology. The authors also report on cocaine-induced 5-HT release in the striatum.

      Strengths:

      This is a topic well worth studying.

      Weaknesses:

      In its current form, this is an incomplete and underpowered study that does little to clarify the complicated relationship that exists between DA and 5-HT in the mammalian brain under physiological conditions or during cocaine use.

      We appreciate the reviewer’s thorough evaluation and constructive efforts. We acknowledge that the original manuscript did not meet the expected standards and have therefore undertaken a complete revision. We hope that the revised version addresses the concerns raised and provides a clearer and more rigorous presentation of our findings.

      Reviewer #3 (Public review):

      The authors suggest that the small release of DA may be due to a release of glutamate from DRN 5-HT neurons to the VTA that stimulates weakly and in a transient fashion the VTA DA neurons, which in the end, produce a transient and small release of DA in the NAc.

      Their findings give more information on the previously reported complex and partial known crosstalk between 5-HT and DA in the NAc.

      I only have some minor concerns about the manuscript:

      (1) In Figure 2F, there is a missing curve for 5-HT in NAc. Besides, the legend shows n=2, making it difficult to perform statistical analysis with that data.

      We repeated the experiments and analysis with a new and expanded dataset. We believe this revised dataset now provides sufficient power for robust statistical analysis.

      (2) In Figure 3, the use of NBQX/AP5 is shown, but it is not mentioned either in the methodology or in the discussion. What is the meaning of those results?

      Based on the reviewers’ suggestions, we have removed this figure entirely from the revised manuscript.

      (3) Line 98 compares results from two different places of stimulation. The results are related to stimulation in the VTA, but the comparison indicates that the stimulation was made in the DRN.

      The manuscript has been extensively revised, and all relevant sections of the text have been updated accordingly.

      (4) If the release of 5-HT in Nac does not occur, it needs to be precise in the abstract that 5-HT is released in the dorsal striatum (DS) but not in the NAc (line 19).

      We performed an additional set of experiments and confirmed the presence of 5-HT release in the NAc.

      (5) Be consistent with the way you mention the 5-HT neurons. For example, in lines from 106 to 119, SERT neurons are used. Previously, 5-HT neurons were used.

      We have carefully considered this issue throughout the revised manuscript and have taken steps to ensure consistency, as highlighted by the reviewer.

      (6) There are several points of confusion when referring to the figures, making the text difficult to follow because the text explains something that is not shown in the figure cited.

      We have carefully considered this issue throughout the revised manuscript and have taken steps to ensure consistency, as highlighted by the reviewer.

      Recommendations for the authors:

      Reviewing Editor Comments:

      Additional comments from the reviewers recommend that the authors increase sample sizes, carry out control experiments to exclude non-specific effects, and modify the title and abstract to reflect the minimal cross-talk observed between dopamine and serotonin. Finally, we note that the text of the manuscript does not meet minimum standards in the field for clarity, interpretability, or reproducibility.

      We acquired a new experimental cohort, and performed additional experiments suggested by the reviewer. The manuscript has been revised and reformatted, incorporating all reviewer comments. We hope that this new version now meets the expected standards for clarity, interpretability, and reproducibility in the field.

      Reviewer #1 (Recommendations for the authors):

      (1) For angled stereotactic injections, the direction of the angle (ML, AP, etc) is not defined.

      Added to the revised manuscript.

      (2) The manuscript is full of grammatical errors and is very difficult to follow in some places, especially the results section for Figures 2 and 3. The following is an incomplete list of the most obvious errors to give examples of what I mean, but I strongly urge the authors to thoroughly revise the entire text.

      (a) There appear to be photometry traces copied and pasted by mistake over the x-axis labels in the bar graph in Figure 1E.

      (b) Line 41 states that fiber photometry recordings were used in reference 17, which is a human study that used voltammetry.

      (c) Figures are referenced out of order throughout.

      (d) Acronyms are used without being defined on the first usage.

      (e) Lines 54-56, missing references.

      (f) Line 82, the crosstalk reported in this paper is unidirectional, not bidirectional.

      (g) Line 92 describes the virus as AAV-dLIGHTX.XX, where presumably the exes are placeholders for numbers that were never replaced.

      (h) The text of the results section for Figure 2 in some cases directly contradicts the data it describes, and likely has some mistaken figure panel references.

      (i) Line 123 is missing a reference and just has the placeholder REF.

      (j) Lines 182-183 are a copy-paste mistake from lines 185-186.

      (k) Scalebars in figure legends are in units of micromolar, not micrometers.

      The manuscript has been extensively rewritten to address and incorporate the reviewers’ comments.

      Reviewer #2 (Recommendations for the authors):

      This is one of the least impressive and compelling manuscripts I have ever been asked to review. It reads in many ways like a first draft, not like a document that was meticulously prepared and proofread prior to submission. The data and analyses are shockingly light (with some conclusions supported by an N of 1 or 2) yet frustratingly difficult to interpret. The text and figures are full of typos, inconsistencies, and factual errors in interpretation. Most concerningly, this study does little to clarify the complicated relationship that exists between DA and 5-HT, including providing compelling evidence for their key finding of 'crosstalk' between both modulators or mechanistically implicating glutamate co-release. In fact, it might add to the noise. It is therefore unclear what the value is of this study in its current form.

      We thank the reviewer for their detailed and thorough comments, which we saw as an opportunity to substantially improve the manuscript. We have therefore undertaken a comprehensive revision to improve clarity, coherence, and overall quality. As part of these revisions, we have removed the original Figure 3 presenting the in vitro patch-clamp electrophysiology data. We hope that the extensive changes implemented in the revised manuscript adequately address the reviewer’s concerns and significantly strengthen the study.

      General comments:

      (1) Limited scope: the design of the experiments performed offers very few opportunities for identifying 'crosstalk' between DA and 5-HT:

      (a) To most, such cross-talk might best be imagined at the level of cells expressing receptors for both modulators, yet this is not studied. Cross-talk may also manifest in complex circuit-level interactions, downstream of brain areas directly targeted by DA and 5-HT. This is not studied either. Instead, only one type of experiment is performed: optogenetically stimulating cell bodies in the midbrain while monitoring DA/5-HT levels in the striatum. Opportunities for 'cross-talk' in this preparation are exceedingly limited. For example, the only way that optogenetically-evoked DA release may impact 5-HT levels (assuming classical, direct projections from DA neurons to the striatum) is via presynaptic modulation of 5-HT release from axons. Though potentially interesting, it is not exhaustive.

      We agree that many types of crosstalk can be investigated; however, we chose to first adopt a broader approach that integrates neuronal circuit-level mechanisms. We believe that an important discrepancy remains in the literature that needs to be addressed. While our findings do not exclude the involvement of other forms of crosstalk, they aim to clarify whether crosstalk already exists at the level of neuronal circuits, which our results support.

      (b) Any negative modulation of DA or 5-HT release would require a baseline tone of either modulator within the dynamic range of the sensor, which is not demonstrated. Thus, in both cases, a negative influence cannot be excluded, limiting any finding to 'positive' modulation. This is problematic for a study set on setting the record straight on 'cross-talk' between DA and 5-HT, given that prior studies pointed to antagonistic influences.

      Previous studies using the same type of sensor have already demonstrated inhibitory crosstalk. For example, the study “Opponent Control of Reinforcement by Striatal Dopamine and Serotonin” successfully used this approach to reveal opposing interactions between dopaminergic and serotonergic signalling. Therefore, our findings are consistent with the possibility of circuit-level crosstalk and further contribute to resolving this ongoing question.

      (c) Only one stimulation paradigm is tested, and its biological (ir)relevance is not discussed.

      The stimulation paradigm used in this study has been extensively validated in multiple publications from our laboratory and has consistently been shown to promote reinforcement. Our recent review provides a comprehensive overview of this approach and its established behavioral effects. (Lüscher et al, Neuron, 2026, PMID: 42462721).

      (d) Only one brain region is investigated (striatum), with little justification for omitting other regions where both modulators coexist (e.g., amygdala, cortex, DRN).

      Not all brain structures can be investigated within the scope of a single study, and we acknowledge this limitation in the Discussion section.

      (e) The suitability of the chosen behavior and cocaine administration to reveal interactions is not mentioned.

      We have now clarified the use of i.p. cocaine injection to study the different temporal dynamics of 5-HT and DA release.

      (2) Scarcity of data and questionable analyses and interpretation:

      We thank the reviewer for the detailed comments listed below (a-m). In response, we repeated many experiments, increased sample sizes, completed the experimental groups, fully reanalysed the dataset, and removed the patch-clamp electrophysiology data. The manuscript has been extensively rewritten and reformatted based on a largely new dataset. In addition, we now focus specifically on one form of crosstalk and have expanded the discussion to address other potential mechanisms, as suggested by the reviewer. We hope this revised version adequately addresses the concerns raised. Because of the many changes in the text, the concerns listed below were addressed.

      (a) Conclusions for Figures 3B and C are based on a N of 1 whole cell recording each.

      (b) The relevance of data in Figures 3D and 3E to test the proposed hypothesis is unclear.

      (c) Figure 2F is an average of N=2 recordings.

      (d) Lines 98-100: Concluding statement not supported by experiment performed.

      (e) Apparent contraction: Lines 112-113 indicate that 5-HT neuron stimulation failed to evoke 'any significant change in DA', while lines 117-118 talk about a small but significant increase in DA in response to the same manipulation.

      (f) Line 114: conclusion not supported by data; Figure 2E points to DA, not 5-HT, and Figure 2F-H only shows 5-HT in DS, not NAc. Legend specifies that only 2 recordings are averaged in Figure 2F, and n=4 are quantified in Figures G and H, despite the plot containing 5 data points. What is going on?

      (g) The key finding of this study is a transient, barely-detectable increase in NAc DA for ~1s during the 10s stimulation of 5-HT neurons in N=4 mice (Figure 2D). It is not clear if a significant increase was observed in all mice tested, or if this increase is significantly different from what is seen in DS. Was this effect seen in multiple testing sessions?

      (h) Concerningly, the authors do not exclude the possibility of a 'startle' response to strong light delivery in mice expressing tdTomato-only. This is an essential control that is missing.

      (i) Very few details regarding this DA response are offered. How soon does it appear following DRN stimulation? If mediated by glutamate co-release, it ought to appear with the very first light stimulus.

      (j) The physiological significance of this stimulation paradigm is not established. Comparing it to cocaine-evoked DA release is not satisfactory, as cocaine elevates DA beyond levels encountered physiologically.

      (k) Correlative evidence to implicate glutamate is insufficient. Does co-release depress when driven with the same stimulus train as that applied in vivo? Is it strong enough to drive DA neuron firing? Does it not affect DS-projecting DA neurons? Does this DA 'blip' disappear in mice genetically deficient in glutamate co-release (using SERT-Cre vglut2-floxed mice, which are commercially available)?

      (l) The authors don't find evidence for DA stimulation affecting 5-HT release in the striatum. Is this sufficient to exclude any influence of DA on 5-HT? Isn't the fact that cocaine evokes 5-HT release evidence of an interaction? As such, it is unclear if the experiments performed significantly advance our understanding of whether and how DA and 5-HT influence one another.

      We respectfully disagree with this statement. Cocaine directly binds to the serotonin transporter (SERT), thereby blocking serotonin reuptake into the presynaptic terminal and increasing extracellular 5-HT levels in the synaptic cleft. Furthermore, dopamine and serotonin release exhibit distinct temporal dynamics, suggesting that their release is mediated through different molecular mechanisms and neural circuit pathways rather than through a single shared process.

      (m) The evidence that 5-HT stimulation alters DA release in the striatum is not compelling, but also not sufficient to exclude or support cross-talk between both modulators. The evidence provided is simply insufficient to strongly validate or challenge earlier studies.

      (3) Minor:

      In this revised manuscript, we have carefully implemented the reviewers’ suggestions and addressed the concerns they raised. We believe that this revised version addresses a significant proportion of the reviewers’ concerns and hope that these fundamental improvements are fully recognized during the evaluation.

      Because of the many changes in the revised manuscript, the concerns listed below do no longer apply or were addressed.

      (a) In the intro paragraph, line 19, specify that you are monitoring DA/5-HT in the striatum of mice.

      (b) In the results section, indicate the preparation: in vivo, awake, what were the mice doing...

      (c) Inconsistent nomenclature: VTA dopamine neurons are referred to as 'VTA DA', while dorsal raphe serotonergic neurons are referred to as 'DRN SERT' or 'DR SERT'. Why the difference? Is there any reason to believe that SERT-Cre mice label non-serotonergic neurons? Please adopt a consistent nomenclature and/or justify your choice. Also, if neurons are optogenetically stimulated, specify the word 'neuron' (e.g., line 118 refers to 'DR SERT stimulation', which could be interpreted to suggest the transporter is being activated).

      (d) Line 103 and elsewhere: Confusing terminology referring to VTA DA neurons stimulation as 'modulating' or 'altering' DA release in the striatum. Isn't it directly evoked by the stimulation of DA-releasing neurons? Please change the terminology so as not to make it unnecessarily vague.

      (e) Lines 111-112: comparison between DA and 5-HT fluorescence devoid of biological meaning. Relative increases in the fluorescence of 2 sensors that are molecularly distinct and separately-expressed in different cohorts of mice cannot be meaningfully compared.

      (f) The timeline in Figure 1A suggests that the optogenetic stimulation protocol is initiated 5 min prior to cocaine injection. Figure 1D (right) plots this 5 min baseline, yet little/no DA release can be seen, despite Figure 1F indicating peak DA ratios of 0.5-1. Please explain. Since both sets of recordings were performed back to back and are directly compared, make sure to show them both on the same graph for direct comparison.

      (g) Figure 1E contains 2 misplaced data traces.

      (h) Figure 2D. Please repeat for the full stimulation window, not just for the few seconds where a tiny change in DA is seen.

      (i) Figure 2E is misleading. It suggests that the peak extracellular DA concentration in NAC following DRN stimulation is approximately half of what cocaine evokes and similar to what VTA DA stim evokes (Figure 1F). But this is simply not true. There must be an error in calculation, and one should wait more than 20 minutes to properly estimate peak DA concentration in cocaine.

      (j) What is the value of comparing optogenetically-evoked neuromodulator release to cocaine-evoked release? Can the authors be sure that the DA levels reached in cocaine are not saturating the DA sensor? Wouldn't this be required to permit comparison?

      (k) Line 123: Please include a citation. Rephrase the sentence to avoid sounding dismissive.

      (l) Line 124: Vague/confusing sentence. What is meant by 'optogenetic projection targeting' and 'parsing the synaptic currents in identified regions'?

      (m) Figures 1D, 1G, 2C, 2F: confirm y-axis unit of measure. Are DA transients really only 0.2% in amplitude?

      (n) Figure 3B, 3C: number of recordings performed in DA and NAcSh, anterior not specified. Only one trace showed, and no summary plot. Insufficient data presented to draw conclusion.

      (o) How does the presence of EPSCs in OFC or NAc help support the author's finding of a brief elevation of DA upon DRN stimulation?

      (p) Line 358: Wrong unit.

      Reviewer #3 (Recommendations for the authors):

      Some minor concerns are the following:

      (1) In line 31, define what DS is.

      (2) In line 57, the word "neurons" is missing in the phrase ".....decreases 5-HT neuron spontaneous firing in DRN,...".

      (3) In line 111, the comparison needs to be between 5-HT release due to optogenetic stimulation with the 5-HT release due to cocaine infusion, not with DA release due to cocaine infusion.

      (4) In line 112, it should be Figure 2F.

      (5) In line 114, instead of 5-HT, it should be DA. In addition, it is not Figure 2E, but 2C.

      (6) In line 123, add the appropriate reference instead of "REF".

      (7) In lines 182 and 183, the text is out of context.

      (8) In lines 250 - 251, the text is out of context.

      (9) Why was Rabit anti-GFP if you do not use GFP in the procedures?

      While the manuscript underwent a complete comprehensive revision to improve clarity, coherence, and overall quality, we have carefully implemented the reviewers’ suggestions and addressed the concerns they raised.

    1. eLife Assessment

      This study demonstrates a critical role of the glycolipid membrane protein insertase (MPIase) in the twin-arginine translocation (TAT) pathway, a protein translocation system that is conserved across all domains of life. The successful reconstitution of the bacterial TAT system in both bacteria-derived and artificial liposomes provides solid experimental evidence that MPIase has a broader role in membrane protein translocation than previously recognized. By revealing the essential function of a nonproteinaceous membrane component in catalyzing a core cellular process, this work offers fundamental insights into the molecular mechanisms of protein translocation.

    2. Reviewer #2 (Public review):

      Summary:

      In this manuscript, the authors investigated the relationship between the Tat system and MPIase, a glycolipid that facilitates protein integration into the bacterial cell membrane. The TAT (twin-arginine translocation) system is a unique membrane transport machinery that exports fully folded proteins containing a twin-arginine signal peptide. Using both in vivo and in vitro approaches, the authors demonstrated that a sufficient amount of MPIase is required for Tat-dependent protein translocation. Furthermore, the authors successfully reconstituted the Tat transport system by combining recombinant TatA, TatB, TatC, MPIase, and FoF1-ATP synthase.

      Strengths:

      The reconstituted system clearly demonstrated the requirement for each component, as substrate translocation occurred only when all components were present. Based on these findings, the authors proposed a mechanistic role for MPIase in facilitating Tat-mediated membrane translocation.

      Previous studies have shown that MPIase is involved in Sec-dependent protein translocation and membrane protein integration, as well as YidC-dependent membrane insertion. The present study further demonstrated that MPIase also plays an essential role in the Tat translocation pathway.

      Overall, this work highlights the central importance of MPIase in bacterial membrane protein biogenesis and provides new insights into the molecular mechanism of Tat-dependent protein transport.

    3. Author response:

      The following is the authors’ response to the previous reviews.

      Public Reviews:

      [Editors' note: Overall, the reviewing editor and reviewers agreed that authors rigorously addressed reviewers' major scientific concerns.]

      Reviewer #1 (Public review):

      Hanako and colleagues demonstrated that glycolipid MPIase is essential for the TAT system, and they successfully reconstituted the TAT system in vitro for the first time. This will facilitate the understanding of the mechanism of the TAT system.

      Comments on revised version from Reviewer 1 and the Reviewing Editor:

      In Reviewer 1's points 1 and 2 regarding statistical significance of immunoblotting data (in recommendation to authors), authors stated that they repeated the experiments three times in the rebuttal letter but not in the figure legends. We recommend authors to add a statement such as "errors denote +-SD (N = 3)" in the final version, which will make the data more solid and reliable.

      Thank you for the comments. We added the suggested sentence.

      Reviewer #2 (Public review):

      Summary:

      In this manuscript, the authors investigated the relationship between the Tat system and MPIase, a glycolipid that facilitates protein integration into the bacterial cell membrane. The TAT (twin-arginine translocation) system is a unique membrane transport machinery that exports fully folded proteins containing a twin-arginine signal peptide. Using both in vivo and in vitro approaches, the authors demonstrated that a sufficient amount of MPIase is required for Tat-dependent protein translocation. Furthermore, the authors successfully reconstituted the Tat transport system by combining recombinant TatA, TatB, TatC, MPIase, and FoF1-ATP synthase.

      Strengths:

      The reconstituted system clearly demonstrated the requirement for each component, as substrate translocation occurred only when all components were present. Based on these findings, the authors proposed a mechanistic role for MPIase in facilitating Tat-mediated membrane translocation. Previous studies have shown that MPIase is involved in Sec-dependent protein translocation and membrane protein integration, as well as YidC-dependent membrane insertion. The present study further demonstrated that MPIase also plays an essential role in the Tat translocation pathway. Overall, this work highlights the central importance of MPIase in bacterial membrane protein biogenesis and provides new insights into the molecular mechanism of Tat-dependent protein transport.

      Comments on revised version from Reviewer 2 and Reviewing Editor:

      (Reviewer 2) It is nice to find the mistake in Fig. 4B (RR, EK413, (not +MPIase)) and corrected it in the revised version. However, the data of Fig. 4B and the related modified text on P.9 L256-269 are not very easy to understand what the authors are trying to argue. This reviewer believes that the magnitudes of MPIase expression level are Ek413 > +MPIase > delta-MPIase. Therefore, the means of supernatant in the delta-MPIase and EK413 represents mostly cytosol and periplasm, respectively. On the other hand, the +MPIase shows less supernatant that means a large part of TorA-GFP are trapped nearby membrane but not aggressively transported. Similar results are found also in (KK) case, whereas the sup level in EK413 is lower than that of (RR) case due to the lack of twin-arginine motif in the substrate.

      Although these results are convincing in explaining the model in Figure 5, authors might be able to attempt to refine them more understandable. Especially, the revised text on P.9 L256-269 is not enough for leading readers to the author's claim. So that, the authors should revise the text about Fig. 4B again and the related description in the manuscript. Another option is it would be possible to delete whole Fig. 4B, because Fig. 4A, C already well support the model in Fig. 5.

      (Reviewing Editor) I agree with Reviewer 2. The first paragraph in the section "MPIase serves as the TAT signal receptor" runs very long and the clarity of the descriptions on Fig. 4B could be further improved. Fig. 4A, C would sufficiently support author's arguments. I recommend that authors consider these options for the final version.

      We appreciate the critical comments. In Fig. 4B, we aimed to show that the amount of membrane-targeted TorA-GFP would increase due to the overproduction of MPIase caused by TatABC. At the same time, TorA-GFP translocation was also stimulated. However, since the translocated TorA-GFP was recovered in the same fractions as the cytosolic TorA-GFP, interpreting the results would be complicated. Therefore, we deleted Fig. 4B, as suggested by the reviewer and editor. The corresponding text and the right-hand side of Fig. S9 were also deleted.

    1. eLife Assessment

      This study is a valuable contribution that comprehensively identifies and characterizes LC3B-binding peptides through a bacterial cell-surface display screen covering approximately 500,000 human peptides. The data presented are solid, although this approach has some limitations. For example, it cannot assess the effects of post-translational modifications, which are often relevant for LIR-mediated interactions. Validation of the newly identified binding peptides by demonstrating their interactions with full-length proteins in cells would further strengthen this work.

    2. Reviewer #1 (Public review):

      Summary:

      This study uses high-throughput bacterial cell surface display to identify LC3B interacting peptides in the human proteome. The screen is unbiased and this type of assay has not previously been used for selecting LC3B interacting peptides. The screen was done with library of 500,000 peptides and they ended up with 427 peptides that they scored as high-confidence LC3B binders. The experiments performed are solid and data are analyzed using well documented methods and statistics.

      The aim for the authors was to isolate LC3B interacting peptides from the human proteome, and the screen succeeded in doing so. The selected set of peptides included several previously reported LIR motifs, but also many novel LC3B binding peptides that either contained or did not contain the canonical core LIR motif [WFY]xx[LVI].

      Another aim was to identify binding determinants important for the LC3B interaction, and they made an interesting sequence logo based on selected LIR containing peptides. This study does not really extend our knowledge related to binding determinants essential for LIR motifs in LC3B binding, but it expands the number of exceptions to the canonical core LIR motif.

      Strengths:

      The approach used here (high throughput bacterial-surface-display) is new. The screen is unbiased and the fact that peptides are directly tested for LC3B binding may facilitate the discovery of non-canonical LIR motifs. The screen appears to be highly selective and manage to distinguish between peptides that interact with LC3B and peptides that do not interact.

      Weaknesses:

      It is a limitation that no proteins are analyzed in this study. Further work is therefore needed to verify that identified LIR motifs are functional in full-length proteins and in cells.

      Comments on revised version.

      It remains a weakness that none of the identified LIR motifs are tested in full-length proteins or cells, but all the specific comments in my initial review have been addressed.

    3. Reviewer #2 (Public review):

      Summary:

      To discover peptides that interact with autophagy-related protein LC3B and profile the key binding determinants, the authors screened a library of ~500,000 36-residue peptides derived from the human proteome using bacterial cell-surface display. Analysis of the screening data revealed exceptions to the reported LIR motif and a strong preference for negatively charged residues adjacent to the LIR.<br /> These results support a refinement the LIR motif definition and expand the network of candidate LC3B interaction partners.

      Strengths:

      High-throughput approach

      Weaknesses:

      Lack of in vitro data and molecular dynamics simulations

      Comments on revised version.

      In the revised manuscript the authors did not provide in vitro and molecular dynamics simulations data.

    4. Reviewer #3 (Public review):

      Summary:

      The LC3 family of proteins, which includes LC3B, are ubiquitin-like proteins that are covalently linked to phosphatidylethanolamine in the expanding autophagosomal membrane during autophagy. LC3 family members bind to short sequences of amino acids that reside within dynamic regions in a wide variety of proteins. These sequences, termed LC3 Interacting Regions (LIRs), were initially thought to function primarily to link LIR containing autophagy cargo receptors to LC3 family members to help facilitate their capture during autophagy. However, the functional importance of LIRs in autophagy has broadened to include more general functions in autophagy as well. While a general consensus for LIR sequences has been described as [FWY]0-X1-X2-[LVI]3 recent work has suggested that additional sequences outside of the canonical LIR sequence can bind LC3 family members and play important roles in autophagy. In this manuscript by Kosmatka et al, the authors perform a high throughput screen using bacterial surface display coupled with fluorescence associated cell sorting to identify which human sequences can bind to LC3B. They identify a variety of peptides capable of binding LC3B including peptides from proteins that have not previously been described as LC3B binding proteins. The results from the bacterial surface display were then used to guide sequence analysis, mutational analysis and structural studies to further characterize the range of LIR sequences that are capable of binding LC3B. Taken together, this work adds to the growing knowledge of how LIR sequences interact with LC3 family members and demonstrates which amino acids both inside and outside of the LIR sequence aid in binding. This work also identifies new potential LC3 binding proteins which may play unknown roles in autophagy regulation. Lastly, this work reinforces the importance of alternative LIR sequences such as the [WFY]0-X1-X2-[WFY]3 sequence which the authors have dubbed the LIR+ sequence.

      Strengths:

      The manuscript uses a robust approach to identify and characterize different peptide sequences that can interact with LC3B. They validate a large number of sequences using biolayer interferometry (BLI). The large number of LC3B binding sequences and their dissociation constants adds significant new information to the field that will help others understand what sequences can bind to LC3B. The authors are also very careful to accurately report on their data and not overly interpret their findings.

      Weaknesses:

      After the authors identify proteins from their bacterial display assay, the remainder of the manuscript is focused on characterizing the different types of sequences that are identified in addition to validating the LC3B-LIR interactions using BLI and X-ray crystallography. However, it's not entirely clear if the screen identified novel LC3B binders that interact with LC3B in cells. While I acknowledge that the focus of the manuscript is on the characterization of LIR sequences that can bind LC3B it seems like a missed opportunity to not validate a few of the novel LC3B binders in vivo.

    5. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This study uses high-throughput bacterial cell-surface display to identify LC3B-interacting peptides in the human proteome. The screen is unbiased, and this type of assay has not previously been used for selecting LC3B-interacting peptides. The screen was done with a library of 500,000 peptides, and they ended up with 427 peptides that they scored as high-confidence LC3B binders. The experiments performed are solid, and data are analyzed using well-documented methods and statistics.

      The aim of the authors was to isolate LC3B-interacting peptides from the human proteome, and the screen succeeded in doing so. The selected set of peptides included several previously reported LIR motifs, but also many novel LC3B binding peptides that either contained or did not contain the canonical core LIR motif [WFY]xx[LVI].

      Another aim was to identify binding determinants important for the LC3B interaction, and they made an interesting sequence logo based on selected LIR-containing peptides. However, this study does not really extend our knowledge related to binding determinants essential for LIR motifs in LC3B binding. They basically verify known characteristics, including the importance of varied types of electrostatic interactions supporting the docking of the core LIR into the LDS of LC3B.

      Strengths:

      The approach used here (high-throughput bacterial-surface-display) is new. The screen is unbiased, and the fact that peptides are directly tested for LC3B binding may facilitate the discovery of non-canonical LIR motifs. The screen appears to be highly selective and manages to distinguish between peptides that interact with LC3B and peptides that do not interact.

      Weaknesses:

      It is a limitation that no proteins are analyzed in this study. Further work is therefore needed to verify that identified LIR motifs are functional in full-length proteins and in cells.

      Reviewer #2 (Public review):

      Summary:

      To discover peptides that interact with autophagy-related protein LC3B and profile the key binding determinants, the authors screened a library of ~500,000 36-residue peptides derived from the human proteome using bacterial cell-surface display. Analysis of the screening data revealed exceptions to the reported LIR motif and a strong preference for negatively charged residues adjacent to the LIR.

      These results support a refinement of the LIR motif definition and expand the network of candidate LC3B interaction partners.

      Strengths:

      High-throughput approach.

      Weaknesses:

      Lack of in vitro data and molecular dynamics simulations.

      Reviewer #3 (Public review):

      Summary:

      The LC3 family of proteins, which includes LC3B, are ubiquitin-like proteins that are covalently linked to phosphatidylethanolamine in the expanding autophagosomal membrane during autophagy. LC3 family members bind to short sequences of amino acids that reside within dynamic regions in a wide variety of proteins. These sequences, termed LC3 Interacting Regions (LIRs), were initially thought to function primarily to link LIR-containing autophagy cargo receptors to LC3 family members to help facilitate their capture during autophagy. However, the functional importance of LIRs in autophagy has broadened to include more general functions in autophagy as well. While a general consensus for LIR sequences has been described as [FWY]0-X1-X2-[LVI]3, recent work has suggested that additional sequences outside of the canonical LIR sequence can bind LC3 family members and play important roles in autophagy. In this manuscript by Kosmatka et al, the authors perform a high-throughput screen using bacterial surface display coupled with fluorescence-associated cell sorting to identify which human sequences can bind to LC3B. They identify a variety of peptides capable of binding LC3B, including peptides from proteins that have not previously been described as LC3B-binding proteins. The results from the bacterial surface display were then used to guide sequence analysis, mutational analysis, and structural studies to further characterize the range of LIR sequences that are capable of binding LC3B. Taken together, this work adds to the growing knowledge of how LIR sequences interact with LC3 family members and demonstrates which amino acids both inside and outside of the LIR sequence aid in binding. This work also identifies new potential LC3 binding proteins, which may play unknown roles in autophagy regulation. Lastly, this work reinforces the importance of alternative LIR sequences such as the [WFY]0-X1-X2-[WFY]3 sequence, which the authors have dubbed the LIR+ sequence.

      Strengths:

      The manuscript uses a robust approach to identify and characterize different peptide sequences that can interact with LC3B. They validate a large number of sequences using biolayer interferometry (BLI) and attempt to correlate different amino acids with their binding affinity for LC3B. The large number of LC3B binding sequences and their dissociation constants adds significant new information to the field that will help others understand what sequences can bind to LC3B. The authors are also very careful to accurately report on their data and not overly interpret their findings.

      Weaknesses:

      After the authors identify proteins from their bacterial display assay, the remainder of the manuscript is focused on characterizing the different types of sequences that are identified in addition to validating the LC3B-LIR interactions using biochemical approaches, including BLI and X-ray crystallography. However, it's not entirely clear if the screen identified novel LC3B binders that interact with LC3B in cells. While I acknowledge that the focus of the manuscript is on the characterization of LIR sequences that can bind LC3B, it seems like a missed opportunity not to validate a few of the novel LC3B binders in vivo. This may result in the demonstration of novel binders of LC3B in cells and may further demonstrate the strength of this approach for identifying LC3 family member binding partners. Therefore, it would be helpful to look at a few proteins identified in the HC set that have not previously been identified as LC3B binders in cells to determine if they CO-IP with LC3B or interact with LC3B using a different approach.

      The reviewers appreciated the contribution of our screen and our biochemical analyses and indicated there would be additional value in performing follow-up studies in cells to investigate the functional relevance of interactions we detected. We agree that such work will be important, but it is beyond the scope of our current study. We expect the data and analyses included in this paper, and the lists of peptide binders that we identified, will inspire such future efforts.

      We appreciate the reviewers’ careful reading of our manuscript, and we have revised the text and figures to improve the clarity and presentation of the data in accordance with their recommendations.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Main comments:

      (1) In the abstract, the authors correctly conclude in the final sentence: "our results refine the LIR motif definition and expand the network of candidate LC3B interaction partners", but I disagree that their study "highlights how mutations at the LC3B-LIR interface can modulate affinity and specificity". They probably refer to the data in Figure 5, where they found that some peptides were positively affected by a mutation of the LDS in LC3B. This is interesting, but four peptides are listed in Figure 5B, and for these peptides, no experimental data are included to verify that their interactions with LC3B are LIR-LDS interactions. LIR-mutated peptides are, for instance, not tested. Therefore, it is possible that these interactions are not LIR-LDS interactions but depend on another binding surface in LC3B than LDS.

      Although LIR-mutated peptides were not tested in the experiments reported in Figure 5B, the LIR docking site (LDS) mutations F52A/L53A change the peptide-LC3B affinity. This suggests that the LDS is involved in the binding interaction, even if the interaction is not with a LIR motif. We have changed the statement to “highlight how mutations at the LIR docking site can modulate affinity and specificity,” to be more explicit.

      (2) Another text line in the abstract is that their study "revealed exceptions to the reported LIR motif and a strong preference for negatively charged residues adjacent to the LIR", and I find this text line a bit misleading. Yes, their structural data for the BLM peptide nicely shows the importance of electrostatic interactions, and R70 in LC3B binds to an E in the +1 position. This is different from several other reported LIR-LDS structures where R70 in LC3B binds to an acidic +7 residue in the LIR motif. However, R70 is conserved in all human ATG8s (R67 in GABARAP) and in many reported ATG8-LIR structures found to interact with an E in the +1 position, exactly as shown for the LC3B-BLM structure shown in Figure 3. The presented structure is therefore a very typical example rather than an exception to previously reported LIR motifs. The authors also select peptides with a so-called LIR+ motif, but this is a previously reported variant, and the alphafold model for the CTSL2 peptide in Figure 4 suggests a quite normal LIR-LDS interaction, except that W is docked into pocket 2.

      While the structural analysis of the BLM peptide bound to LC3B supports the importance of electrostatic interactions, it is not the primary basis for the statement regarding a “strong preference for negatively charged residues adjacent to the LIR.” Rather, this conclusion results from the sequence features enriched across the selected peptides in the HC-set, which revealed a consistent enrichment of negatively charged residues flanking the core motif and in the +4 position.

      With respect to the statement that the study “revealed exceptions to the reported LIR motif,” the intent was not to suggest that these interactions are unprecedented in the literature, but rather that we expanded the number of known exceptions to the most canonically defined LIR motif ([FWY]xx[LVI]). We do not claim these are unprecedented; we cite prior examples of exceptions in the text (Li 2018, Knævelsrud 2013, Chatzichristofi 2023). To avoid confusion, we revised the abstract to state that the study “revealed exceptions to the canonical LIR motif ([FWY]xx[LVI]).”

      (3) A limitation related to the search for binding determinants can be that the screen is only performed with LC3B. The impact of several determinants beyond the core LIR is known to be ATG8 specific. For instance, LC3-specific R10 and R11 are known to form electrostatic interactions with acidic residues in positions -2, -3, and -4 relative to core LIR, and the -3 and -4 positions can be a determinant for LC3 binding (Rogov et al, 2023; Autophagy Rep. 2: 2188523). Here, the authors made a consensus peptide based on the sequence logo derived from HC-set (Figure 3C). It is interesting that this peptide had a very high affinity for LC3B (0,1 µM) that was found to depend on the D in position -3. In the sequence logo shown in Figure 3A, D, and E are strongly favored in position -3, but position -4 is not included in the logo. Are acidic residues similarly enriched in the -4 position?

      The high affinity of the consensus peptide for LC3B is not dependent on the D in position -3. The peptides pCONS<sub>W-Nterm-S</sub> (EEDDDDEDWEILDIDEDEDGSGNEFYE) and pCONS<sub>W-Nterm-S-D-A</sub>(EEDDDAEDWEILDIDEDEDGSGNEFYE), where D in position -3 is mutated to A, have the same affinity (0.12 µM). The truncated peptide, pCONS<sub>W-Nterm-L</sub> (EDWEILDIDEDEDGSGNEFYE), which lacks position -3, has a weaker affinity of 0.66 ± 0.1 µM. These results illustrate that the flanking acidic residues, rather than specifically D in position -3, contribute to affinity (Figure 3C, Supplementary Table 3). The logo corresponding to the entire consensus peptide is shown in Supplementary Figure 5A. Acidic residues are not as enriched in the -4 position as they are in positions -1 through -3, although D is still the predominant residue in position -4 and is thus present in the consensus peptide.

      Extending this type of screen and analysis to other ATG8 family members has the potential to illuminate interesting paralog-specific differences.

      (4) Will the full list of peptides in the HC-set be included in a final manuscript? This is a very interesting set of peptides for scientists interested in LC3B-interacting proteins, and this includes those interested in noncanonical LIR interactions.

      Yes, all Supplementary Files (1-5) are available at Zenodo (10.5281/zenodo.17943925). Supplementary File 4 contains all of the peptides that were enriched through the sixth sort, and those peptides where “Average Z-score Across Sorts 4-6” is greater than or equal to (≥) 1.70 correspond to the full list of peptides in the HC-set.

      Minor comments:

      (5) When reading the result section, it is confusing that the bacterial membrane protein is named "circularly permuted OmpX" in the text on page 2, and "eCPX" in the figure legend. I suggest including both names in at least one place, as done in the methods section.

      We changed the text on page 2 to use the full name, which is “enhanced circularly permuted OmpX (eCPX)”, to address the confusion.

      (6) Figure S3A: The list of 10 peptides in the input library that have a KD in the LIRcentral database includes 7 with a KD below 5 µm (if I am correct), but is said to be 6 in the text on page 3.

      We corrected the text on page 3 to 7 peptides.

      (7) Error in the text. Data for the BLM peptide is shown in Figure 2B, but replaced by MAP1B in the text on page 3.

      We tested four peptides reported to co-immunoprecipitate with LC3B without an experimentally validated LIR motif (MAP1B<sup>823-858</sup>, ATG4A<sup>363-398</sup>, SCYL1<sup>640-675</sup>, and HEAT3<sup>377-412</sup>). Of these four, binding of three (ATG4A<sup>363-398</sup>, SCYL1<sup>640-675</sup>, and HEAT3<sup>377-412</sup>) is illustrated in Figure 2B, in addition to BLM<sup>552-587</sup>, which has not been previously reported to co-IP with LC3B. We added a note that not all curves are shown. Dissociation constants for these 5 peptides are included in Supplementary Table 1.

      Reviewer #2 (Recommendations for the authors):

      Human MAP1LC3B (LC3B) binds proteins involved in autophagy and other cellular processes using a short linear motif known as the LC3-interacting region (LIR).

      To discover peptides that interact with LC3B and profile the key binding determinants, the authors screened a library of ~500,000 36-residue peptides derived from the human proteome using bacterial cell-surface display. Analysis of the screening data, coupled with structural studies and site-directed mutagenesis, revealed exceptions to the reported LIR motif and a strong preference for negatively charged residues adjacent to the LIR.

      The authors also suggest that mutations in LC3B, shown to abrogate binding of LIR-containing peptides, instead alter LC3B binding specificity, leading to enhanced binding of some LIR-containing sequences.

      This is an interesting study, well structured, with solid data.

      My major concern is that the conclusion about the effect of known LC3B mutations on the binding of LIR-containing proteins is not strongly supported by the presented data. Did the authors try molecular dynamics simulations in such LC3B mutations to examine if F52A/L53A substitutions create alternative hydrophobic interfaces that modulate LIR motif peptide binding in a sequence-dependent manner?

      Figure 5C includes data that demonstrate that mutations to the LIR docking site (F52A, L53A) change the binding affinity of several peptides. Our structural analysis of the F52A/L53A LC3B mutants using ChimeraX (Figure 5A) provides a model for the changes in the hydrophobic pockets and a possible explanation for how these mutations introduce altered hydrophobic contacts and sequence specificity at the LDS. It would be challenging to exhaustively sample binding modes for peptides with different sequences using MD, which is best suited for exploring local minima. We do not claim to have a detailed structural explanation for the observed changes in binding specificity.

      Additionally, in vitro cellular data on the expression and localization of some of their strongest LC3B binding proteins (and their relevant novel residue mutations) would be useful.

      Assessing the expression and localization of our strongest LC3B-binding proteins and testing the effects of novel residue mutations in cells would be a valuable follow-up to further explore the implications of our findings. However, these experiments are beyond the scope of the current study, which focuses on high-throughput characterization of LC3B binding determinants using biochemical methods. We close the paper with a statement highlighting this as a promising direction for future investigation.

      Finally, the suggested refinement of the LIR motif sequence is not easily identifiable in the main text.

      We added a clause to clarify this in the Discussion: “Although a small number of LC3B-binding peptides bearing this LIR+ motif have been reported (~1% of cataloged hAtg8-binding peptides) (Li et al., 2018; Chatzichristofi et al., 2023), their prevalence appears underappreciated given their relatively high frequency in the HC-set (Supplementary Figure 10), leading to the refined motif [FWY]xx[LVIFWY].”

      Reviewer #3 (Recommendations for the authors):

      Please find my specific comments below.

      (1) The authors identify a large number of peptides from their screen, including several strong binders as shown in Figure 2C, that appear to bind LC3B but are from proteins that have not been previously annotated as LC3B binders in cells. Therefore, it would be helpful to validate whether some of these proteins are indeed LC3B binders in cells using CO-IPs or another related technique. This would significantly strengthen the manuscript, further validate their approach for characterizing LIR LC3B binding interactions, and demonstrate novel potential autophagy regulators.

      Validating candidate LC3B binders in cells using co-immunoprecipitation or related techniques will be a valuable follow-up and could provide important insight into novel autophagy regulators. Such experiments are beyond the scope of the current manuscript, which focuses on the high-throughput characterization of LC3B binding determinants in vitro. We mention this in the discussion as a promising avenue for future investigation.

      (2) In Figure 2C, there are two proteins that have high mean-Z scores but no detectable binding to LC3B (top right quadrant). It would be helpful for the authors to acknowledge this and speculate on why binders are isolated but cannot have detectable binding. Is this expected for a screen of this size? Is this below the non-specific binding expected or above? 

      Some apparent false-positive binders may have been isolated and exhibit high mean Z-scores due to avidity effects from the pre-tetramerized LC3B used in the bacterial display screen. Binding was validated using monomeric LC3B in BLI experiments, which eliminates these multivalent effects. Consequently, weak interactions stabilized by LC3B tetramerization that enrich in the bacterial display assay may not be detectable at the monomeric LC3B concentrations tested in BLI. Given the differences in multimerization between the screening and validation assays, as well as the large size of the library, both false positives and false negatives are expected. We added a comment about the apparent false positives to section Strongly enriched peptides include novel candidate LC3B interactors.

      (3) In Figure 2C, it says four peptides are colored as in 1C, but I can only see three colored that way, and the yellow one is very faint and hard to see.

      We corrected the text in Figure 2C to three peptides and have darkened the yellow point for easier visualization.

      (4) The authors determine the crystal structure of a peptide from BLM bound to LC3B. However, no electron density is shown in the manuscript. Therefore, it is unclear how ordered the side chains are that are shown, including for D564-D566. The authors should include an electron density omit map for the BLM peptide so that readers can accurately interpret how ordered each side chain is in the peptide.

      Supplementary Figure 9 shows an ensemble of conformations consistent with the diffraction data, as well as an electron density omit map. Data to support visualization of the electron density for the structure is included with the PDB deposition (structure 9p3e).

      (5) In Figure 3C, the authors include numbers in the bar graph. What are the numbers that are included? Is this the mean Kd value? If so, the numbers may be misleading without a second significant figure since some values of 0.1 are far away from 0.2, while others are very close to 0.2. In addition, if these values are the Kd values, it would be helpful to include these values in all other bar graphs throughout the manuscript, including Figures 4A and 5C.

      The values reported are the mean K<sub>D</sub> values, in micromolar, which is now indicated in the legend of Figure 3C; we also added a second significant figure. The mean K<sub>D</sub> values have also been included for the bar graphs in Figures 4A and 5C.

      (6) On page 7, the authors state that "we found strong evidence for enrichment of LIR sequences, but not for these closely related motifs, indicating that whereas such LIR-related motifs may support binding in some sequence contexts, they are not sufficiently strong binding determinants to be enriched in our screen". I found this statement to be unclear. Do the authors mean that while LIR sequences were strongly enriched in the HC-set, LIR+ sequences were not? This statement should be clarified.

      The statement on page 7 is in reference to Supplementary Figure 10, which shows that peptides containing the canonical LIR [FWY]xx[LVI] are enriched in the HC-set relative to their presence in the input library. In contrast, peptides containing LIR-related motifs ([WFY]xx[WFY], [LVI]xx[FWY], and [LVI]xx[LVI]) are not strongly enriched in the HC-set relative to their presence in the input library. We clarified this statement to read “We found strong evidence that canonical LIR motifs ([FWY]xx[LVI]) are enriched in the HC-set relative to their frequency in the input library, whereas LIR-related motifs ([WFY]xx[WFY], [LVI]xx[FWY], [LVI]xx[LVI]) are not (Supplementary Figure 10), indicating that although LIR-related motifs can support LC3B binding in certain sequence contexts, they are not sufficiently strong determinants to drive enrichment in our screen.”

      (7) How different is the alphafold prediction of the LIR+ engaging with LC3 compared to the LIR LC3 interaction? Are they largely similar or are there unique features to each? Does the presence of a second large aromatic residue push the backbone of the peptide farther away from the hydrophobic binding pocket?

      To address this, we predicted structures of the LIR+ motif of CTSL2 (EEDWEVFDPY) and a canonical LIR version (EEDWEVIDPY) to LC3B using AlphaFold3. Structural superposition shows that LC3B adopts a conserved conformation in both complexes (Cα RMSD 1.55 Å). The backbones of the core LIR / LIR+ motif also align closely to each other (1.39 Å RMSD), indicating preservation of the canonical binding mode. The LIR+ peptide exhibits a modest increase (~2 Å) in Cα distance between the aromatic residue Phe and LC3B F52 in HP2 compared to the corresponding Ile in the canonical LIR (8.3 Å vs 6.3 Å). Thus, a subtle backbone repositioning is predicted to accommodate the aromatic side chain. We added a comment about this to the section Highly enriched peptides lacking a canonical LIR can bind LC3B.

    1. eLife Assessment

      This valuable study substantially improves the phylogenetic framework for Kinetoplastea through greatly expanded sampling of free-living neobodonids; the phylogenomic analyses provide convincing evidence that neobodonids are paraphyletic and establish a substantially revised framework for deep relationships within Metakinetoplastina. The work forms the foundation for future comparative studies of kinetoplastid evolution and systematics, although its implications for the origins of parasitism should not be overstated. An 18S rRNA phylogeny documenting the placement of the newly sampled strains within previously recognized neobodonid lineages and/or confirmation of the principal topology using a well-documented phylogenomic set of marker genes would further strengthen the study. The work will be of particular interest to researchers studying protist evolution and the evolutionary context of trypanosomatids.

    2. Reviewer #1 (Public review):

      Summary:

      The authors present the most comprehensive phylogenomic study of free-living kinetoplastids to date, including newly generated transcriptomic data from 11 species. This dataset provides a solid foundation for future comparative evolutionary studies of kinetoplastids, a group that includes important human pathogens such as Trypanosoma brucei.

      Strengths:

      The study is clearly written, and the analyses are thoroughly described, well documented, and reproducible. The phylogenomic dataset comprises 444 proteins, which is larger than those used in many comparable phylogenomic studies. The main strength of the study is the substantially improved taxon sampling of neobodonids, which provides a much stronger framework for resolving evolutionary relationships among free-living kinetoplastids.

      Weaknesses:

      The phylogenomic analysis is based on a newly assembled dataset comprising 444 single-copy orthogroups. Using an established phylogenomic dataset, expanded to include the newly studied species, to confirm the inferred relationships would provide additional support for the robustness of the recovered topology.

    3. Reviewer #2 (Public review):

      Summary:

      This is a highly competent and very throough phylogenomic study of the position of neobodonids within the Kinetoplastid phylogeny. The addition of new transcriptomic data for diverse neobodonids allows their relationships to be better understood, leading to convincing changes to neobodonid taxonomy.

      Strengths:

      The generation of transcriptomic data produces more phylogenetic characters than ever before from several, diverse neobodonid lineages. This represents a substantial increase in the data available for this relatively unstudied group. The methodology used to define the character set and then estimate the phylogeny is conventional, very thoroughly described and expertly executed. The phylogeny is highly robust, and we can be very confident of the result.

      Weaknesses:

      These changes to neobodonid taxonomy, or essentially the discovery that some 'neobodonids' actually represent distinct lineages at the base of the Metakinetoplastida, are not surprising. So undersampled are the Kinetoplastida, both in terms of molecular markers and phyletic diversity, that an investment in sampling on this scale is always likely to bring about changes. The significance of these changes is not immediately apparent. Since they do not concern the Eubodonidae, they have no implications for the trypanosomatids and the new Metakinetoplastid lineages are very distant from the origins of parasitism, such that comments on "preadaptations for disease" are not compelling.

    4. Reviewer #3 (Public review):

      Summary:

      The manuscript by Zavadska et al. presents a phylogenomic study focused on the relationships among metakinetoplastids, specifically addressing the question whether Neobodonida are monophyletic. The authors collected eleven kinetoplastid strains affiliated with five out of six neobodonid clades identified in previous studies and sequenced their transcriptomes. These data along with SRAs available from public databases were processed via assembly, annotation and decontamination. The final dataset, representing all (non-trypanosomatid) kinetoplastids with genome and/or transcriptome data available as well as selected trypanosomatids and discoban outgroups, was used for protein orthology inference and identification of single-copy orthologs present in the majority of taxa, followed by phylogenetic analysis in maximum likelihood and Bayesian frameworks.

      Strengths:

      I find the new data and resulting phylogenetic reconstruction valuable and interesting, particularly given the paucity of genomic/transcriptomic data from free-living kinetoplastids. The study is well-designed methodologically, and the main finding (paraphyly of Neobodonida) appears indisputable, considering the provided phylogenimic evidence. Following best practices, the authors shared their primary data, scripts, and files resulting from the conducted analyses.

      Weaknesses:

      Several aspects of the presentation and interpretation would benefit from revision. In particular, the composition of the neobodonid lineages central to the study is insufficiently explained, and an 18S rRNA gene-based tree showing these lineages and the placement of the newly analysed strains would substantially improve the clarity of the study. The Introduction would also benefit from a more concise and better-structured account of previous phylogenetic evidence and a clearer explanation of the broader biological significance of kinetoplastids, while the Methods could be streamlined by removing redundant information and presenting some details in tables. Finally, the rationale for including the electron microscopy images in the supplement should be clarified, as these are currently not discussed in the text and represent only a single studied strain.

    5. Author response:

      Reviewer #1 (Public review):

      Summary:

      The authors present the most comprehensive phylogenomic study of free-living kinetoplastids to date, including newly generated transcriptomic data from 11 species. This dataset provides a solid foundation for future comparative evolutionary studies of kinetoplastids, a group that includes important human pathogens such as Trypanosoma brucei.

      Strengths:

      The study is clearly written, and the analyses are thoroughly described, well documented, and reproducible. The phylogenomic dataset comprises 444 proteins, which is larger than those used in many comparable phylogenomic studies. The main strength of the study is the substantially improved taxon sampling of neobodonids, which provides a much stronger framework for resolving evolutionary relationships among free-living kinetoplastids.

      Weaknesses:

      The phylogenomic analysis is based on a newly assembled dataset comprising 444 single-copy orthogroups. Using an established phylogenomic dataset, expanded to include the newly studied species, to confirm the inferred relationships would provide additional support for the robustness of the recovered topology.

      We thank the reviewer for their comments on our work. We appreciate the suggestion to confirm the topology that we recovered by using an established phylogenomic dataset. We note that we did not choose to use an established dataset for the main phylogenomic analysis, as we found that free-living kinetoplastid orthologs are systematically excluded from commonly used “universal pan-eukaryotic” datasets (e.g., Fig. S4), despite the fact that we were able to identify sufficient orthologous proteins to robustly reconstruct interspecies relationships among euglenozoans. In our revised manuscript, we will implement the reviewer’s suggestion by comparing our orthogroup set with commonly used pan-eukaryotic orthogroup sets.

      Reviewer #2 (Public review):

      Summary:

      This is a highly competent and very throough phylogenomic study of the position of neobodonids within the Kinetoplastid phylogeny. The addition of new transcriptomic data for diverse neobodonids allows their relationships to be better understood, leading to convincing changes to neobodonid taxonomy.

      Strengths:

      The generation of transcriptomic data produces more phylogenetic characters than ever before from several, diverse neobodonid lineages. This represents a substantial increase in the data available for this relatively unstudied group. The methodology used to define the character set and then estimate the phylogeny is conventional, very thoroughly described and expertly executed. The phylogeny is highly robust, and we can be very confident of the result.

      Weaknesses:

      These changes to neobodonid taxonomy, or essentially the discovery that some 'neobodonids' actually represent distinct lineages at the base of the Metakinetoplastida, are not surprising. So undersampled are the Kinetoplastida, both in terms of molecular markers and phyletic diversity, that an investment in sampling on this scale is always likely to bring about changes. The significance of these changes is not immediately apparent. Since they do not concern the Eubodonidae, they have no implications for the trypanosomatids and the new Metakinetoplastid lineages are very distant from the origins of parasitism, such that comments on "preadaptations for disease" are not compelling.

      We agree that comments and discussion on how our study may shed light on the preadaptations of trypanosomatids to parasitism were excessive. We were influenced by prior studies of free-living metakinetoplastids, which highlighted their importance and applied value in the context of understanding evolution of parasitism in kinetoplastids (Deschamps et al., 2011; Dolezel et al., 2000; Flegontova et al., 2017). In our revised manuscript, we will remove/shorten the corresponding parts of discussion and results, and place more emphasis on the relevance of our study to understand the multiple origins of parasitism in metakinetoplastids, not just trypanosomatids.

      The datasets published in our study can be used in the future to investigate, for example, how gene repertoire of trypanosomatids differs from the free-living species, or how specific gene families evolved, and whether their diversification/origin predated the origin of the largest parasitic clade. We acknowledge that the data from the closest free-living relative of trypanosomatids that was available before our study is already extremely valuable, and that our datasets would be of most use for reconstructing relatively ancient preadaptations.

      Reviewer #3 (Public review):

      Summary:

      The manuscript by Zavadska et al. presents a phylogenomic study focused on the relationships among metakinetoplastids, specifically addressing the question whether Neobodonida are monophyletic. The authors collected eleven kinetoplastid strains affiliated with five out of six neobodonid clades identified in previous studies and sequenced their transcriptomes. These data along with SRAs available from public databases were processed via assembly, annotation and decontamination. The final dataset, representing all (non-trypanosomatid) kinetoplastids with genome and/or transcriptome data available as well as selected trypanosomatids and discoban outgroups, was used for protein orthology inference and identification of single-copy orthologs present in the majority of taxa, followed by phylogenetic analysis in maximum likelihood and Bayesian frameworks.

      Strengths:

      I find the new data and resulting phylogenetic reconstruction valuable and interesting, particularly given the paucity of genomic/transcriptomic data from free-living kinetoplastids. The study is well-designed methodologically, and the main finding (paraphyly of Neobodonida) appears indisputable, considering the provided phylogenimic evidence. Following best practices, the authors shared their primary data, scripts, and files resulting from the conducted analyses.

      Weaknesses:

      Several aspects of the presentation and interpretation would benefit from revision. In particular, the composition of the neobodonid lineages central to the study is insufficiently explained, and an 18S rRNA gene-based tree showing these lineages and the placement of the newly analysed strains would substantially improve the clarity of the study. The Introduction would also benefit from a more concise and better-structured account of previous phylogenetic evidence and a clearer explanation of the broader biological significance of kinetoplastids, while the Methods could be streamlined by removing redundant information and presenting some details in tables. Finally, the rationale for including the electron microscopy images in the supplement should be clarified, as these are currently not discussed in the text and represent only a single studied strain.

      We thank the reviewer for their suggestions on how our manuscript could be improved. Regarding the selection of species, our study followed a common procedure in protistology: we attempted to isolate, culture and sequence as many free-living kinetoplastids as possible, and the resulting manuscript includes all the cases in which we were successful. We appreciate the reviewer’s suggestion of including an 18S rRNA gene tree, which we will implement in our revised manuscript. We will revise the introduction to improve the presentation of previous phylogenetic evidence in metakinetoplastids and to provide a more clear explanation of their broader significance. We also plan to reduce redundant information in the Methods and relocate information on strains and culturing conditions to tables.

      Electron microscopy images in the Supplement display the ultrastructure of an isolate from the 1D clade. They were included because the information on morphology of this clade remains scarce in comparison to other clades; thus, we added these images because we believe they could be useful for the community. We intend to clarify this in our revised manuscript.

      References

      Deschamps P, Lara E, Marande W, López-García P, Ekelund F, Moreira D (2011) Phylogenomic analysis of kinetoplastids supports that trypanosomatids arose from within bodonids Mol Biol Evol 28:53-58 https://doi.org/10.1093/molbev/msq289

      Dolezel D, Jirků M, Maslov DA, Lukes J (2000) Phylogeny of the bodonid flagellates (Kinetoplastida) based on small-subunit rRNA gene sequences Int J Syst Evol Microbiol 50:1943-1951 https://doi.org/10.1099/00207713-50-5-1943

      Flegontova O, Flegontov P, Malviya S, Poulain J, de Vargas C, Bowler C, Lukeš J, Horák A (2018) Neobodonids are dominant kinetoplastids in the global ocean Environ Microbiol 20:878-889 https://doi.org/10.1111/1462-2920.14034

    1. eLife Assessment

      This valuable study applies a hidden Markov model to freezing, ultrasonic vocalizations, and lever pressing in male rats to ask whether fear extinction restores normal behavior or only suppresses defensive responding. The evidence is incomplete because the model states are not related back to raw behavior or group manipulations, and the states are not tested as predictors of relapse. The work will be of interest to researchers studying fear extinction and computational approaches to behavior.

    2. Reviewer #1 (Public review):

      Summary:

      This study addresses an important question of how recovery from conditioned fear can be characterized beyond the reduction of defensive responses. The authors combine multiple behavioral measures with a theory-guided hidden Markov model to identify recurring behavioral modes and examine how the organization of behavior changes during fear conditioning and extinction. The study provides an interesting and potentially valuable framework for examining recovery from conditioned fear as a broader change in behavioral repertoire rather than simply as a reduction in defensive behavior.

      Strengths:

      A major strength of the study is the attempt to characterize fear-related behavior as a dynamic organization of multiple behavioral measures rather than relying on a single conventional measure such as freezing. The integration of freezing, ultrasonic vocalizations, and food-reinforced behavior into recurring behavioral modes provides a potentially useful approach for capturing behavioral states that may be missed by analyses of individual responses. The study also presents a theoretically motivated framework for distinguishing defensive, quiescent, exploratory, and goal-directed behavioral modes, which could provide a useful conceptual basis for studying functional recovery following aversive experiences.

      Weaknesses:

      The main concern is whether the analyses and behavioral definitions provide sufficient support for the interpretation of these behavioral modes and, in particular, for the proposed concept of functional recovery. First, the data-driven model selection indicates substantially better predictive performance for eight states, whereas the authors retain four states primarily to conform to their theoretical framework. A stronger justification for prioritizing the four-state solution would therefore strengthen the interpretation of the results. Second, it is difficult to determine from the current behavioral measures whether the Quiescent state reflects safety and rest or behavioral inhibition associated with residual fear or anxiety. The marked reduction in exploratory and goal-directed behavior could potentially be consistent with either interpretation, making it important to consider alternative explanations for this state.

      The definition of functional recovery also warrants further consideration. The authors propose the reinitiation of food-seeking behavior in the previously threatening environment as an important indicator of recovery. From an evolutionary and ethological perspective, however, an animal that has experienced a threat while foraging in a particular location may benefit from shifting its foraging to an alternative location rather than returning to the previously threatening location, even after extinction learning. Thus, the resumption of goal-directed behavior in the previously threatening environment may not necessarily represent a biologically appropriate benchmark for successful fear extinction or functional recovery, as functional recovery may involve substantial changes in motivational processes that cannot be captured by the simple return of a previously suppressed behavior. More broadly, the results may support the value of examining behavioral repertoire during extinction, but they do not yet establish that restoration of the broader repertoire, or specifically the return of goal-directed behavior in the previously threatening environment, is required for functional recovery.

      Finally, the use of the no-shock control group could be more informative. The observed reduction in exploratory behavior during conditioning cannot by itself distinguish associative fear learning from habituation to novelty. A direct comparison between shock and no-shock groups would therefore help determine whether the changes in behavioral modes are specifically related to conditioning.

      Overall, the study provides a potentially useful framework and an interesting conceptual perspective on recovery from conditioned fear. However, the strength of the evidence supporting the interpretation of the identified behavioral modes and the proposed definition of functional recovery is currently limited. Addressing these interpretational and analytical issues would strengthen the manuscript and help establish the extent to which this framework provides a meaningful advance in the study of fear extinction and recovery.

    3. Reviewer #2 (Public review):

      Summary:

      The authors aimed to develop and validate a preclinical computational framework for assessing functional recovery after aversive learning. They argue that a reduction in defensive behavior (e.g., freezing) is insufficient to conclude recovery, and that a comprehensive assessment must also track the re-emergence of exploration and goal-directed action. To test this, they adapted a multimodal paradigm in rats, measuring freezing, 22-kHz and 50-kHz ultrasonic vocalizations (USVs), and food-reinforced lever pressing. Using a theory-guided hidden Markov model (HMM) with four states derived from tri-reference-point theory (Fear/Danger, Quiescent, Exploratory, Goal-directed), they decoded behavioral trajectories across fear conditioning, extinction, delay, and contextual relapse experiments.

      Strengths:

      (1) The study tackles a significant gap in animal models of fear and trauma-related disorders. It correctly identifies that a focus on a single defensive response like freezing may be misleading and that a broader behavioral assessment is necessary to understand the impact of interventions.

      (2) The application of a hidden Markov model to combine multiple behavioral channels into a cohesive, theory-driven framework is a strength. This approach moves beyond simple summary statistics to capture the temporal dynamics of behavior and allows for the identification of distinct, recurring states.

      (3) The authors demonstrate commendable rigor in their model selection and validation. They employ discrete-aware scoring to address the boundary issues of count data and conduct extensive stability analyses across restarts, variance floors, and studies. The association of the Fear/Danger mode occupancy with fecal boli, an independent autonomic measure, provides a convincing external validation.

      (4) The study provides several important findings. For instance, it shows that during the first extinction session, although Fear/Danger occupancy declined, the dominant state was Quiescent, rather than Exploratory or Goal-directed. This directly supports the core premise that the absence of fear does not equal functional recovery.

      Weaknesses:

      (1) The authors transparently report that their results are not emission-family invariant. The failure of count-native models (e.g., negative binomial) to converge or produce the expected four-state solution is a major limitation. This suggests the theory-guided state structure is tied to the specific Gaussian representation chosen and may not be a robust and universal feature of the behavioral data. This limits the strength of the claim that the data naturally resolve into these four modes.

      (2) The exclusive use of male rats from a single laboratory limits the generalizability of the findings to female animals and other experimental settings. The cross-study recurrence analysis is a helpful sensitivity check but does not substitute for true model transfer to an independent, out-of-sample dataset.

      (3) Testing K=4 vs. K=8 raises the question of whether the four-state solution is the most appropriate level of abstraction for the questions being asked.

      (4) The interpretation of the 50-kHz USV band as an Exploratory state is somewhat broad. 50-kHz calls are emitted in a variety of contexts, including social interaction and reward. While the use of the band is justified, it remains an indirect measure of exploration and should be interpreted with caution. The interpretation and link to the exploratory state should be toned down and discussed.

    4. Author response:

      We thank the editors and both reviewers for their careful evaluation and for recognizing the value of examining the wider behavioral repertoire after aversive learning. Our aim is to follow what an individual does after danger and intervention, rather than infer recovery from a decline in one defensive response. The reviews identify where the present computational account needs stronger connections to observed behavior, experimental comparisons, and subsequent outcomes. We are preparing a substantive revision addressing these issues. This provisional response outlines the work planned; it does not report completed revisions or new findings.

      Response to Reviewer 1:

      (1) Four-state versus eight-state resolution

      We agree that the better held-out performance of K=8 must be central to the interpretation. K=4 is a theory-guided coarse descriptive scale, not the predictively optimal model or evidence that behavior contains exactly four natural states. We will explain this distinction more clearly and show what the additional states resolve, particularly within the low-event-output configurations. Comparisons of experimental change at both resolutions will help establish which conclusions depend on the coarse scale and which persist at finer resolution. We will not treat agreement within one emission representation as proof of a universal four-mode organization.

      (2) Interpreting Quiescent

      The reviewer's concern is important: Quiescent denotes low vocal and lever output, not an absence of freezing, a safe internal state, or successful recovery. Likewise, a decline in Fear/Danger occupancy does not establish that defensive responding has disappeared. We will present raw freezing, vocalizations, and lever pressing alongside state occupancy, with experimental groups distinguished. The higher-resolution subdivision of count-silent observations will make differences in freezing more visible. These comparisons can describe residual defensive behavior, but cannot by themselves determine whether quietness reflects rest, inhibition, anxiety, or another internal condition. We will retain these alternatives explicitly rather than infer safety from silence.

      (3) The ethological meaning of functional recovery

      We appreciate the distinction between returning to a previously rewarded action and adapting appropriately after danger. Resuming food-directed responding in the original setting is one task-specific outcome, not a necessary or sufficient definition of recovery. Avoiding that location or pursuing resources elsewhere could also be adaptive. The present task does not offer the alternatives needed to distinguish those possibilities. We will therefore frame the measured repertoire as an assay of context-dependent functional change, not a universal standard to which animals should return. The translational question remains whether an intervention changes the broader organization of behavior, but judging successful adaptation also requires evidence about the opportunities and risks available to the individual. Pooled state percentages will not be presented as normal or desirable recovery targets.

      (4) Shock versus no-shock comparisons

      We will report direct comparisons between shock and no-shock animals for the raw channels and inferred occupancies, including changes over conditioning. This will allow the reader to distinguish changes associated with aversive conditioning from changes shared with unshocked animals, such as familiarization or declining novelty. The revision will also separate extinction from time-matched exposure and present renewal by group rather than pooling treatment histories. Comparisons will use rats as the experimental units, report uncertainty, and make differences in observation coverage explicit.

      (5) Connecting post-extinction behavior to later outcomes

      We will examine whether the available paired records permit associations between post-extinction configurations and subsequent defensive responding, alongside the corresponding raw behavioral measures. These analyses will respect test chronology and intervening treatment. They will be identified as retrospective and exploratory, not as independent validation of individual prediction: the current shared representation was fitted using the full dataset. We will report limited sample size, missing modalities, or insufficient variation when these prevent a question from being estimated. A failure to establish an association will not be treated as evidence that no biological relationship exists. If these data cannot support a prognostic claim, we will state that limitation and identify the prospective test needed.

      Response to Reviewer 2:

      (1) Emission-family dependence

      We agree that the count-native sensitivities materially limit the interpretation. Alternative emission models also retain temporal dependence; their disagreement with the Gaussian model therefore does not show that temporal modeling uniquely produces the four labels, or that the Gaussian account is biologically privileged. We will explain the count transformation and scaling, and distinguish numerical fit eligibility from recovery of the complete behavioral-label configuration. We will also make clearer which findings are conditional on the Gaussian representation. Stable classification within that representation is useful evidence for a reproducible reference, but is not evidence for an emission-family-invariant taxonomy.

      (2) Generalizability and independent transfer

      All animals were male and the experiments came from one laboratory. Recurrence across related study datasets, including a retest of the same animals, does not substitute for transfer to independent subjects or settings. We will strengthen the prospective validation roadmap: fix preprocessing and model parameters before testing independent animals, include both sexes and an independent setting, measure individual baselines, and evaluate later outcomes without using those outcomes to fit the representation. Direct exploration measures and tests of instrumental outcome sensitivity would provide independent checks on functional interpretation. This is a proposed validation design, not generalization demonstrated by the present study.

      (3) The appropriate level of abstraction

      The revision will distinguish predictive resolution from interpretive scale throughout, as described in our response to Reviewer 1. K=8's advantage will remain explicit. The relevant question for K=4 is whether it offers an informative coarse summary of experimental change, not whether it wins model selection. Conclusions that require a finer distinction will be presented at that resolution rather than forced into four categories.

      (4) Vocal and instrumental labels

      We agree that 50-kHz calls are a context-sensitive vocal measure, not a direct observation of exploration or a unique indicator of positive affect. We will qualify the Exploratory label consistently by its measured emission pattern. Similarly, food-reinforced lever pressing establishes instrumental responding in this task; without outcome-sensitivity tests it does not establish goal-directed rather than habitual control in the technical sense. The labels will describe observed behavioral configurations, not subjective feelings, neural mechanisms, or independently validated motivational states. A simplified behavior-to-model mapping will make these distinctions easier to follow.

      Together, these revisions will place the raw behavior and experimental contrasts alongside the model, clarify the limits of the labels and recovery interpretation, and make the next validation steps concrete. The intended contribution is a reproducible framework for describing history-dependent changes in the measured repertoire and testing their functional relevance, without equating a model category with recovery or claiming individualized prognosis that the present evidence has not established.

    1. eLife Assessment

      This useful study presents Penumbria, a method for segmenting cells in three-dimensional microscopy images, released with publicly available code and documentation. The implementation is technically sound, and the reported performance gains would be a welcome contribution; the advance is incremental, since regression on distances to boundaries is already widespread in the field. The evidence is incomplete: the main text does not state which setting it used for the main competing method, and the setting used is the one that handles data of this kind poorly, so the central claim of better accuracy is unsettled; current leading methods are missing from the comparison; and five architectural and training choices are combined without attributing the gains to any of them. Every evaluation dataset labels nuclei, so the work should present itself as nuclei segmentation; describing and completing the comparisons, and attributing the gains, would raise the strength of evidence.

    2. Reviewer #1 (Public review):

      Summary:

      The manuscript proposes an architecture and distance-regression-based logic for 3D cell segmentation.

      The architecture combines a U-Net backbone with an xLSTM bottleneck layer and two novel components: a Zernike Phase Layer that is used for input normalization and the Scaled Grid Geocaps that link the input and bottleneck, inspired by the Capsule mechanism.

      During training, the network predicts foreground vs. background and regresses a distance transform as output. During inference, these predictions are used as input to a watershed procedure to segment cells in 3D.

      The method is evaluated by training on four different 3D cell/nucleus segmentation tasks and compared to StarDist3D and CellPoseSAM baselines, which are also trained on the respective datasets using the same train / val / test splits.

      The authors report improved segmentation quality compared to the baselines.

      Strengths:

      The experiments are solid; ablation studies evaluate the main architectural components, and the software appears to be well documented (I did not have time to test it).

      Weaknesses:

      (1) This is not really a weakness but a general point: the main issue in microscopy segmentation is that a lot of training data is needed if training a network from scratch. This results in a very laborious data annotation process. This is what modern approaches like CellPoseSAM and microSAM address by providing strong pre-trained networks that work well out-of-the-box for many imaging settings and can then be fine-tuned with a few corrected images to achieve state-of-the-art results. Having a better 3D segmentation approach is of course still worthwhile, but does not address the main practical challenge. I would suggest clarifying this in the introduction.

      (2) The authors implement a well-known strategy to segmentation in microscopy: regressing the distance transform. They acknowledge some prior work (Scherr et al., Lux and Matula), but this approach is more prevalent than acknowledged, including for 3D cell segmentation. For example, microSAM [1] implements cell instance segmentation by regressing the distance to the cell boundary and centroid, which is applied in 3D by CochleaNet [2]. See also [3] for an overview of different distance- or flow-field-regression-based cell segmentation approaches. I would suggest performing a more thorough literature review and acknowledging more prior work.

      (3) The baseline comparisons are not described in any detail, are questionable for CellPoseSAM, and lack current state-of-the-art models:

      3.1) The details on training and inference for the baseline methods are missing. This is particularly missing for CellPoseSAM. Here, it would be important to clarify whether the model is initialized randomly or with CellPoseSAM's pretrained weights and which of the two different 3D segmentation modes is used (see next point). It would also make sense to report both CellPoseSAM in zero-shot mode and finetuned for all cases.

      3.2) CellPoseSAM supports two different 3D segmentation modes: either a "2.5D" segmentation mode, where the network is applied across all three axes, the resulting flow maps are merged, and then used for segmentation. And an "overlap-based stitching mode", where segmentation is applied only in the XY plane and slices are then merged based on overlap. It is unclear which of these modes is used. Ideally both should be reported; the "2.5D" mode often leads to worse results for anisotropic data, and we almost always prefer the overlap-based stitching in practice.

      3.3) Two important state-of-the-art baselines are missing:

      i) microSAM [1], which is widely used for cell segmentation based on a pretrained 2D network that is extended to 3D using a similar strategy as CellPoseSAM's overlap-based stitching mode. It would make sense to report both zero-shot and fine-tuned performance.

      ii) uSeg3D [4]: this method implements a generalized 3D stitching approach that works independently of the underlying architecture and reports significant improvements.<br /> Improving and extending the baseline experiments would give a much better picture of the practical relevance of this work.

      References:

      [1] https://www.nature.com/articles/s41592-024-02580-4

      [2] https://www.biorxiv.org/content/10.1101/2025.11.16.688700v1.abstract

      [3] https://doi.org/10.6084/m9.figshare.32782521

      [4] https://www.nature.com/articles/s41592-025-02887-w

    3. Reviewer #2 (Public review):

      Summary:

      In their manuscript "Penumbria: Advanced 3D cell segmentation for biomedical imaging", the authors describe their 3D nuclei segmentation pipeline and compare it against state-of-the-art methods for nuclei and cell segmentation.

      The introduction is clear, with state-of-the-art methods, for instance, segmentation of cells from microscopy images, and motivates the method with science that can be done with cell segmentation. Related work is appropriately mentioned with the caveat that it conflates cell and nuclei segmentation, and some related work is missing.

      The Results section describes the loss, the architecture novelties, and evaluation results on nuclei segmentation, not cell segmentation. Here it becomes clear that Penumbria addresses 3D nuclei segmentation, i.e., instance segmentation of medium-sized, mostly convex-ish and well-separated objects in single-channel 3D volumes.

      Penumbria combines several choices and innovations:

      - loss: boundary distance regression<br /> - architecture<br /> - 3D U-VixLSTM<br /> - Zernike Layer<br /> - SGG<br /> - combine multiple scales in SGG<br /> - sampling strategy to look for bright and dark objects.

      The authors show on four nuclei segmentation benchmarks from confocal images that Penumbria outperforms CellPose-SAM (in either zero-shot or fine-tuned mode) and Stardist-3D in most cases, except for some where Stardist-3D performs better. They report results of an IoU threshold sweep to measure instance segmentation performance.

      Penumbria is available on GitHub under a non-commercial license. I have not tested it yet, and therefore assume that the code is in full working condition and reproduces the reported results.

      Strengths:

      (1) The method may outperform state-of-the-art methods for cell segmentation as shown in three of four 3D benchmarks.

      (2) The code is available.

      Weaknesses:

      Major

      (1) Similar to the established confusion across the literature, all the authors' evaluation datasets do not label cells, but cellular nuclei. This is important to understand because the methods they compare against, CellPose-SAM and Stardist-3D, demonstrate their performance on a variety of datasets, some of which label cellular membranes instead of nuclei. The cell segmentation task is often considered harder than the nuclei segmentation task, because objects consistently touch each other, the separation may have noise-induced holes, and object shapes may be more diverse than for nuclei.

      (2) The addition of U-VixLSTM, SGG, multi-scale input, Zernike layers, and distance transform regression sounds great, but a comparison to architectures or conventional losses used to train any combination is missing. Five innovations/choices combined simultaneously without an attempt to attribute.

      (3) There is no clear delineation of 2D and 3D approaches. E.g. CellPose-SAM is not 3D, but sequential 2D execution with optional averaging of doing this along all axes.

      (4) For the 3-fold cross-validated evaluation: It sounds like they were all trained for each benchmark independently? Did Stardist-3D and CellPose SAM start from their existing 'general' weights or from scratch (not possible with SAM, I know). Not described at all.

      (5) Experiments to evaluate generalization performance are missing (train on all datasets, compare across datasets, ...).

      (6) The Zernike layer optimizes only coefficients {3,4,12}; the evaluation could be described as rudimentary. Table 3 is missing cross-validation. Since mouse_skull is (a lot) different and differences for the others are minor, I would be curious how significant these results are. The following text is unclear: "This reflects how we developed these modifications in turn." How so?

      (7) Training and inference: input and output block size missing, GPU memory requirements undefined, discuss block size vs. batch size. 50% overlap block-inference with feathering not compared to other overlap ratios, just "works best". Test-time augmentation 8x rotation (what are those rotations? Why 8? How much does it contribute? Compare the cost for 8xs test time augmentation against skipping "background" heuristic.)

      Minor

      (1) What is the incoming contrast range and bit depth?

      (2) Stardist-3D's weakness is not that it requires convexity (it requires star-convexity) but that it picks one hypothesis leading to low-resolution outlines. This has been overcome by Starfinity (https://linkinghub.elsevier.com/retrieve/pii/S0092867421013398; https://github.com/multiFISH/EASI-FISH).

      (3) Heatmap regression is not the same as distance regression; the manuscript does not explain what it is and uses terms interchangeably. This should be clarified. The reference for heatmap is 14; for distances, there are others, e.g. doi:10.1007/978-3-030-00934-2_36.

      (4) Only one evaluation example compares against CellPose-SAM zero-shot; I would add this to all of them. The mentioned place switch would be interesting to see in a plot instead of text.

      (5) "We replicated this failure on multiple machines." How does the machine matter here? Did the authors do cross-validation across different computers, instead of different random seeds? This sounds expensive!

      (6) Brightness-based sampling could be achieved by augmentation?

      (7) In the text "We use the following bicubic upsampling factors[...]", I think the authors mean tricubic, or cubic (because they upscale only one axis). Bicubic makes no sense, but people say this in the literature; we can do better. Why integer upscaling factors instead of exact ones? Are the authors concerned about that one incomplete border pixel?

      (8) "Furthermore, it is not confined to Linux-based environments such as Segmamba [32], an important consideration given that biologists often work with Windows machines."<br /> This is confusing. Is this a comment about somebody's publicly available repository? This is not relevant to evaluate the method and should be discussed separately. Does it work in WSL?

      (9) Downsampling by factors >2 with bicubic interpolation sounds wrong; should the authors use averaging?

    4. Author response:

      We thank the reviewers for their thoughtful comments. We will address the substantive concerns by broadening our evaluation to additional 3D datasets, strengthening comparisons against competitive baselines, and systematically ablating Penumbria's architectural components. We are confident that these experiments will establish Penumbria as a state-of-the-art 3D instance-segmentation method across a substantially broader range of segmentation challenges in biological imaging, without the pronounced dataset-specific failure modes observed for several competing approaches.

      Scope and dataset difficulty.

      We will expand our benchmark to include additional 3D datasets spanning nuclei, whole cells, and organelles. However, we do not accept two assertions made in the review: first, that nuclear segmentation is inherently easier than whole-cell segmentation; and second, that our existing datasets contain “mostly convex-ish and well-separated” objects (which we address separately below). Parhyale illustrates why the first assertion is unwarranted: despite being a nuclear-label dataset, its nuclei are extremely densely packed, with boundaries frequently so poorly distinguishable that even manual identification of individual instances is challenging. Historically, StarDist-3D has been the clear state-of-the-art method on this dataset, with little effective competition, whereas Penumbria matches its performance without imposing a star-convex shape prior. Conversely, the PlantSeg study reported an Adapted Rand error of only 0.075 on 3D cell-wall-labelled leaf tissue and approximately 90–99% correctly segmented whole cells across six leaf-surface specimens [1].

      Segmentation difficulty is an empirical property, not a consequence of what is being segmented or how it is stained.

      Diversity in biological targets or staining modalities does not necessarily translate into diversity of segmentation challenges, let alone greater difficulty. Constructing a meaningful benchmark therefore requires expert judgment in selecting datasets spanning challenging conditions, including irregular morphology, crowding, poor contrast, anisotropy, and imaging artifacts, informed by previous benchmarking results. The same empirical standard must apply to the reviewers' characterization of our datasets as “mostly convex-ish and well-separated.” Neither characterization is supported by quantitative evidence or relevant citations, and even if both were established, they would not by themselves demonstrate that the datasets are easy to segment. We will therefore quantitatively characterize the datasets, including their morphology and crowding, and examine how these properties relate to segmentation performance across methods.

      Zero-shot generalization.

      We will evaluate Cellpose-SAM zero-shot across all datasets, using segmentation accuracy (SA) at IoU 0.5 below 0.30 as a deliberately permissive indicator of severe instance-segmentation failure. Even at this threshold, substantial merging or missed instances can make the output unsuitable for reliable instance-level quantitative analysis without extensive correction.

      Cell pose-SAM comparison and baselines.

      We appreciate that the reviewers have since acknowledged the baseline training configurations reported in our Supplementary Material, which specified that Cellpose-SAM was fine-tuned from pretrained weights, while StarDist-3D was trained from scratch. In retrospect, these details would have benefited from greater visibility in the main text, and we will move them there in the revision. We also identified an important omission in our Cellpose-SAM inference configuration. The Cellpose-SAM paper specifies 1,000 dynamics iterations, flow3D_smooth=2, and removal of masks below 1,000 pixels for its 3D experiments. These settings differ substantially from generic API defaults and materially improve performance on several of our datasets. We will therefore repeat the evaluation using the published 3D settings and transparently report both the original and corrected configurations, with the latter serving as the principal comparison where applicable.

      u-Segment3D.

      The reviewers recommend u-Segment3D because it “reports large improvements,” without specifying over which methods. Its comparisons with Cellpose involve earlier cyto models, not Cellpose-SAM, which was introduced nearly a year after u-Segment3D's original preprint. Moreover, u-Segment3D is a 2D-to-3D aggregation framework rather than an independent segmentation model. Its supplementary material reports dataset-specific gradient smoothing and a 200-voxel size filter, without documenting equivalent settings for its Cellpose 3D baseline. As our own experiments demonstrate, such settings can materially affect 3D accuracy. The reported improvements therefore do not establish competitiveness with a properly configured and fine-tuned Cellpose-SAM. We will instead prioritize StarDist-3D, micro-SAM, and a corrected Cellpose-SAM comparison, reporting all inference configurations transparently.

      Architectural testing.

      While distance-transform regression is an established technique, as acknowledged in our manuscript, Penumbria's principal methodological contributions lie in its architecture and the integration of its components. We will expand our ablations by removing key architectural elements individually and measuring the resulting changes in performance. These experiments will quantify the contribution of each element within the complete pipeline. We look forward to submitting the revised manuscript incorporating these improvements.

      (1) Wolny, A., Cerrone, L., Vijayan, A., Tofanelli, R., Barro, A. V., Louveaux, M., et al. (2020). Accurate and versatile 3D segmentation of plant tissues at cellular resolution. eLife, 9, e57613.

    1. eLife Assessment

      This important study uses coarse-grained molecular simulations to investigate how phosphorylation and small-molecule ligands reshape the interactions underlying condensation and multidomain assembly of HDAC6. The findings provide mechanistic insights into how modifications of intrinsically disordered regions and ligand binding may regulate protein condensation. The evidence is solid: although the simulations broadly agree with previous experimental observations, concerns about model robustness and sampling in full-length assemblies limit support for the proposed mechanism and its pharmacological implications.

    2. Reviewer #1 (Public review):

      Summary:

      The manuscript authors have used Martini 3-based CG simulations to study how S22 phosphorylation and two catalytic-pocket inhibitors, Nexturastat A and HPOB, regulate HDAC6 condensation.

      In isolated IDR1 slabs, phosphorylation lowers the apparent saturation concentration without compacting single chains, and the manuscript attributes this to new contacts between the phospho-S22/D26 region and Arg/Lys-rich patches. The two ligands perturb these contacts differently. In full-length multichain systems, the C-terminal FD3 domain is the most frequent contact partner, and the ligands shift contacts from FD3-FD3 toward pocket-FD3. The manuscript proposes a two-layer mechanism by which pocket-binding ligands regulate multidomain condensates.

      Strengths:

      The question is well motivated by published experiments, the simulated IDR1 construct matches the experimental one, the phosphorylation contact pattern recurs across independent salt conditions, and the Discussion offers testable predictions. My main reservations are whether the full-length results are robust to the model and to sampling, and whether the evidence reaches the mechanistic and pharmacological conclusions drawn from it.

      Weaknesses:

      (1) The full-length conclusions rest on FD3-FD3 being the dominant domain contact. The water-interaction correction applies only to the disordered regions, so the folded domains retain standard Martini 3 behavior, which is known to overestimate protein association (e.g., Thomasen et al., J. Chem. Theory Comput. 2022). The contact maps are also normalized per bead, which favors a small domain such as FD3. Please test whether FD3-FD3 dominance survives a folded-domain correction, and show raw contact counts next to the normalized maps. Without this test, and without experimental evidence that this domain self-associates, frame the FD3 result as a model prediction.

      (2) The ligand-treated IDR1 slabs are single trajectories with no statistical uncertainty on the saturation concentrations or on the contact-difference maps. The full-length systems have three replicates; contact numbers are still rising at the end of several trajectories (Figure S9), and most differences between Nexturastat A and HPOB overlap within error. The wild-type controls also reverse the ligand ordering (Figs. S12, S14), which suggests that trajectory-to-trajectory variation may drive the domain-level differences. Please add independent replicas for the ligand comparisons, show convergence over consecutive time blocks, and report uncertainties on the difference maps. Claims should then be limited to the differences that are resolved.

      (3) The Discussion presents the simulations as explaining why Nexturastat A suppresses HDAC6 condensation more strongly than HPOB. However, every full-length system assembles at a high protein concentration, and total contacts and morphologies are similar across conditions, so the simulations do not show suppression. The IDR-level and domain-level mechanisms are studied in separate systems, and ligand distribution and IDR1 contacts are not analyzed in the full-length assemblies, so the coupling of the two layers named in the title is not tested. Please add an analysis that bears on condensation propensity and on this coupling, or recast "explains" as "is consistent with" and revise the title and abstract accordingly.

      (4) For phosphorylation, the IDR1 fragment is strongly basic, so a simple reduction of net charge could lower the saturation concentration without a position-specific contact node. The phosphoserine charge state used is also lower than expected at neutral pH. Moving the same charge to a distal serine, and testing the fully charged phosphoserine, would distinguish these readings. For the ligands, the coarse-grained models show only weak pocket engagement, lack a non-pocket control, have unvalidated nonbonded parameters, and are simulated at millimolar concentrations. The ligand-IDR1 mechanism could therefore reflect nonspecific association. Please add a non-pocket surface control, validate the ligand parameters, and relate the simulated concentrations to those of the motivating experiments.

    3. Reviewer #2 (Public review):

      Summary:

      The authors investigated the phosphorylation- and ligand-dependent regulation of HDAC6 condensates using explicit-solvent coarse-grained simulations. They found these regulations can be attributed to the reorganization of the interaction networks between multiple domains within the condensate.

      Strengths:

      Large-scale condensate simulations with multiple domains using explicit-solvent models are nontrivial, impressive, and provide a useful example of what can currently be studied using such simulation models.

      Comparison between simulations with/without ligand and with/without phosphorylation at different salt concentrations provides a comprehensive framework for understanding conditional residue- and domain-level condensate organization.

      The simulation results are mostly consistent with previous experimental observations and offer potential mechanistic understandings that extend those findings.

      The proposed FD3-centered interaction network for phosphorylation- and ligand-dependent regulations is a novel hypothesis.

      The study generates a few experimentally testable predictions regarding the IDR- and FD3-mediated interactions.

      Weaknesses:

      When going through the manuscript, especially the result section, I sometimes found it challenging to distinguish what has already been reported experimentally (mostly from Lu et al., Nature Cancer) from what is newly learned from the simulations. I do appreciate that the authors mentioned which results reproduce experimental trends; however, this is often after all simulations. It may help if each subsection first briefly states the known experimental observation and then explains the specific question addressed by or new mechanisms learned by the simulation. For instance, experiments have shown that phosphorylation enhanced and ligand suppressed phase separation.

      The authors directly used Automartini to generate the ligand parameters and then checked the contact fraction between the ligand and the pocket. Such analysis only tells whether the ligand visits the pocket but does not test either specificity or binding affinity, and therefore does not support the claim of ligand pocket recognition. The simulation concentration is also in the millimolar regime, probably much higher than the experimental range, so it's not surprising to see ~10% to 20% contact fraction. Considering that no native interactions are used to restrain the ligand-pocket interactions, I suspect the ligand might also have similar contacts to the other regions of HDAC. The authors might want to be careful about their discussions.

      The claim regarding the IDR dimension might be problematic. It's still an open question whether a coarse-grained model, or even current all-atom force fields, can reliably resolve the effect of a single phosphorylation on IDR dimensions. I agree that all current results, including the error estimate, support that Rg do not change that much, but the authors might still want to be careful about such claim considering such small perturbation in the sequence.

      The proposed electrostatic rewiring is based on the difference between the contact maps of WT and phosphorylation variant. It looks like the difference is relatively small to the contact probabilities. There is also no error estimation or pattern validation from multiple simulations. The authors might want to quantify the uncertainties of such patch-level relative changes by either checking some independent simulations or time-block variations.

      A similar comment applies to the claims on the charged-contact reorganization mechanism of two small inhibitors on LLLPS. Error estimates should be provided for these contacts when comparing different variant. In cases when errors are large (e.g. contact number with errors reported in Fig. 4K to N), statistical analysis (p-values) is needed to show the significance of the results.

      Regarding the proposed "phospho-S22-centered" contact network, I had some difficulty recognizing the specific regions discussed in Figures 2 and 3. It might be helpful to mark these regions directly on the contact maps. The authors may also wish to consider whether "S22-centered" is the most accurate description, or whether the observed changes are better described as involving a broader segment spanning several residues around S22. And since phosphorylation is introduced at S22, observing changes near S22 is not unexpected and may not be sufficient to establish phospho-S22-centered network.

      Within the last result section, the authors argued that FD3 plays an important role in both phosphorylation- and ligand-regulated HDAC assembly. However, only contact-based analysis instead of saturation concentration was presented. I understand the substantial challenge of obtaining converged saturation concentration for such system with multiple domains, if so, the authors need to discuss such caveats.

      Though direct experimental evidence of such FD3-centered interaction network does not exist, the experimental paper did provide measurements for a couple domain-deletion constructs, including the deletion of the FD3 (BUZ) region. This might be worth discussing, considering the manuscript focuses on the role of FD3 region. In particular, deletion of the BUZ region did not appear to affect LLPS. This does not necessarily exclude a role for FD3 in phosphorylation- or ligand-dependent regulation. However, it may complicate the description of FD3 as the center of the condensate network. The authors might want to discuss how their model can be reconciled with these domain-deletion results and consider their corresponding conclusion.

      Finally, the authors describe their mechanism as coupled networks. They might want to clarify the coupling they meant. If this refers to the coupling between different regions (e.g. S22 and FD3) or domains, they might want to consider more quantitive analysis, for instance by conditional/correlated contacts between domains. Changes in contact patterns at different conditions do not necessarily show the coupling between domains.

    4. Reviewer #3 (Public review):

      Summary:

      In this work, the authors probe the combined role of phosphorylation of an intrinsically disordered region (IDR) and domain pocket interactions with small molecules on the assembly of a multidomain protein HDAC6 using molecular simulations. Their find that IDR phosphorylation and ligand interactions with domain pockets can together reshaped in the interaction networks; thereby, expanding mechanistic understanding of multidomain protein assembly in the context of phase separation beyond an IDR centric grammar. The work is supported by extensive simulations featuring several controls and analysis of the underlying interaction networks.

      Strengths:

      (1) They find an important role of phosphorylation and ligand binding in shaping multidomain protein assembly.

      (2) They demonstrate that even if IDR:IDR interactions are important in isolated IDR systems, folded domain-folded domain interactions can dominate in the full-length protein. Furthermore, ligands can significantly perturb the interaction landscape, e.g., by redirecting folded domain-folded domain interactions to domain pocket ones.

      (3) They also show that different ligands can yield differing impact on the interaction networks by both competing with and compensating interactions to varying degrees. This is demonstrated for two ligands (Nexturastat A and HPOB).

      (4) They probe both the IDRs in isolation (including both the phosphorylated and unphosphorylated versions) and condensates formed via these IDRs. They also characterize assemblies of the full length proteins (with and without phosphorylation) in presence and absence of the ligands. The combined simulations, in general, support the key conclusions of the paper.

      Weaknesses:

      (1) There are areas where statistical tests can improve the quantitative presentation of the work. Specifically, the difference in the IDR ensembles for the isolated chains should be assessed for statistical significance.

      (2) There appears to be considerable finite-size effects in some of the slab simulations that may affect the conclusions. In particular, the chains along the long axis appear to be interacting across the periodic boundary. This can happen if the slab long axis is not long enough/if the ratio of dilute phase to dense phase is not large enough.

      (3) Some decisions in the simulation conditions require further justification/explanation. More specifically, the concentrations of small molecules used, as well as the decision to conduct simulations at 295 K, should be adequately justified.

      (4) Given the size of the full-length protein, it is understandable that the full dense phase is not probed for the full-length protein. However, it would be useful to comment on how the conclusions may change in the context of the full length. For instance, there is no real interface that the small molecules need to diffuse across and so discussions on the role of partitioning in the paper should be better contextualized in light of this.

    5. Author response:

      Reviewer #1 (Public review):

      (1) The full-length conclusions rest on FD3-FD3 being the dominant domain contact. The water-interaction correction applies only to the disordered regions, so the folded domains retain standard Martini 3 behavior, which is known to overestimate protein association (e.g., Thomasen et al., J. Chem. Theory Comput. 2022). The contact maps are also normalized per bead, which favors a small domain such as FD3. Please test whether FD3-FD3 dominance survives a folded-domain correction, and show raw contact counts next to the normalized maps. Without this test, and without experimental evidence that this domain self-associates, frame the FD3 result as a model prediction.

      We thank the reviewer for highlighting this important concern. We agree that the apparent prominence of FD3–FD3 interactions in the full-length simulations requires careful examination, particularly because the folded domains retain the standard Martini 3 protein–protein interaction strength whereas the water-interaction correction used in this study was applied to the disordered regions. We also agree that per-bead normalization may influence the apparent relative contributions of domains of different sizes.

      In the revised manuscript, we will examine the robustness of the FD3-centered interaction pattern to the treatment of folded-domain interactions. We will also report raw contact counts alongside the normalized contact maps and explicitly evaluate how domain size and normalization affect the relative contact frequencies.

      More generally, we agree that the simulations alone do not establish FD3 self-association as an experimentally validated property of HDAC6 condensates. We will therefore distinguish more clearly between simulation-derived predictions and experimentally established conclusions and will frame the proposed FD3-centered interaction network as a model prediction where appropriate.

      (2) The ligand-treated IDR1 slabs are single trajectories with no statistical uncertainty on the saturation concentrations or on the contact-difference maps. The full-length systems have three replicates; contact numbers are still rising at the end of several trajectories (Figure S9), and most differences between Nexturastat A and HPOB overlap within error. The wild-type controls also reverse the ligand ordering (Figs. S12, S14), which suggests that trajectory-to-trajectory variation may drive the domain-level differences. Please add independent replicas for the ligand comparisons, show convergence over consecutive time blocks, and report uncertainties on the difference maps. Claims should then be limited to the differences that are resolved.

      We agree that additional statistical characterization is required for the ligand-dependent comparisons. In particular, the current ligand-treated IDR1 slab simulations do not provide sufficient information about trajectory-to-trajectory variability, and differences between Nexturastat A and HPOB in the full-length simulations should not be interpreted without considering the uncertainty across independent trajectories.

      In the revised manuscript, we will rigorously assess the convergence and statistical robustness of the current simulations, including time-block analyses of the saturation concentrations and contact patterns. We will additionally perform independent replicate slab simulations to evaluate the robustness of the estimated saturation concentrations and interaction patterns. We will also increase the independent sampling of the full-length multichain systems to better characterize trajectory-to-trajectory variability and the robustness of the domain-level interaction patterns. Together, these analyses will allow us to provide uncertainty estimates and determine which ligand-dependent differences are reproducibly resolved.

      We will also reassess the differences attributed to Nexturastat A and HPOB in light of these uncertainties. Where the differences are not statistically resolved, we will refrain from assigning ligand-specific mechanistic interpretations and will revise the corresponding conclusions accordingly.

      (3) The Discussion presents the simulations as explaining why Nexturastat A suppresses HDAC6 condensation more strongly than HPOB. However, every full-length system assembles at a high protein concentration, and total contacts and morphologies are similar across conditions, so the simulations do not show suppression. The IDR-level and domain-level mechanisms are studied in separate systems, and ligand distribution and IDR1 contacts are not analyzed in the full-length assemblies, so the coupling of the two layers named in the title is not tested. Please add an analysis that bears on condensation propensity and on this coupling, or recast "explains" as "is consistent with" and revise the title and abstract accordingly.

      We agree that the current full-length simulations do not directly demonstrate suppression of condensation by either ligand. All full-length systems were simulated under conditions in which assemblies form, and similarities in the overall contact numbers and morphologies do not provide a direct measure of differences in saturation concentration or phase-separation propensity.

      We will therefore revise the title, Abstract, Results, and Discussion where necessary to avoid implying that the full-length simulations directly explain the experimentally observed suppression of HDAC6 condensation. Instead, we will describe the simulated interaction-network changes as being consistent with, or as providing a possible molecular basis for, the experimentally observed effects.

      We also agree that the coupling between the IDR-level and domain-level regulatory mechanisms needs to be more clearly established. We will extend the analysis of the full-length simulations to examine ligand distributions and interactions involving IDR1 and the folded domains and will assess whether conditional or correlated contact patterns provide quantitative evidence linking these interaction networks. If such analyses do not provide sufficient evidence for direct coupling, we will revise the terminology accordingly and describe the results as condition-dependent reorganization of multiple interaction networks rather than as a demonstrated coupled mechanism.

      (4) For phosphorylation, the IDR1 fragment is strongly basic, so a simple reduction of net charge could lower the saturation concentration without a position-specific contact node. The phosphoserine charge state used is also lower than expected at neutral pH. Moving the same charge to a distal serine, and testing the fully charged phosphoserine, would distinguish these readings. For the ligands, the coarse-grained models show only weak pocket engagement, lack a non-pocket control, have unvalidated nonbonded parameters, and are simulated at millimolar concentrations. The ligand-IDR1 mechanism could therefore reflect nonspecific association. Please add a non-pocket surface control, validate the ligand parameters, and relate the simulated concentrations to those of the motivating experiments.

      We thank the reviewer for raising these important points regarding the interpretation of both the phosphorylation- and ligand-dependent effects.

      For the phosphorylation-dependent mechanism, we would like to clarify that our simulations were motivated by prior experimental observations showing that phosphorylation at different serine residues of HDAC6 does not produce equivalent effects on phase separation. In particular, phosphorylation at S22 was found to have a pronounced effect on HDAC6 phase separation, whereas phosphorylation at the other tested serine sites did not show a comparable effect. These experimental observations argue against a simple model in which the effect of phosphorylation is determined solely by an increase in the overall negative charge of the protein, and instead indicate that S22 represents a site-specific regulatory position.

      Our simulations were therefore designed to explore the molecular basis of this experimentally established site specificity, rather than to establish S22 specificity from the simulations alone. In this context, the enhanced contacts involving the phosphorylated S22-containing region and positively charged segments of IDR1 provide a possible molecular explanation for why phosphorylation at this particular site can have a distinct effect on phase separation. We will clarify this experimental motivation and the scope of our interpretation in the revised manuscript, and will avoid implying that the simulations alone demonstrate the site specificity of S22 phosphorylation.

      We nevertheless agree that the representation of the phosphoserine charge is an important modeling consideration. We will examine the sensitivity of the phosphorylation-dependent results to the phosphoserine charge state and clarify the corresponding limitations of the coarse-grained representation in the revised manuscript.

      We also agree that the ligand simulations require a clearer assessment of specificity. In the revised manuscript, we will compare ligand contacts with the catalytic pocket to contacts with non-pocket regions of HDAC6 and further evaluate the extent of nonspecific protein association. We will also validate the physical reasonableness of the coarse-grained ligand parameterization, with particular attention to the nonbonded parameters relevant to ligand-protein interactions, and report the validation in the revised manuscript. We will explicitly state the limitations of using these models to infer binding specificity or affinity

      Finally, we will clarify the relationship between the ligand concentrations used in the simulations and those used in the motivating experiments. The high ligand concentrations used in the simulations were chosen to obtain sufficient sampling of ligand-protein encounters on accessible simulation timescales, and we will make clear that these simulations should not be interpreted as reproducing experimental binding equilibria at the corresponding concentrations.

      Reviewer #2 (Public review):

      When going through the manuscript, especially the result section, I sometimes found it challenging to distinguish what has already been reported experimentally (mostly from Lu et al., Nature Cancer) from what is newly learned from the simulations. I do appreciate that the authors mentioned which results reproduce experimental trends; however, this is often after all simulations. It may help if each subsection first briefly states the known experimental observation and then explains the specific question addressed by or new mechanisms learned by the simulation. For instance, experiments have shown that phosphorylation enhanced and ligand suppressed phase separation.

      We agree that the manuscript should more clearly separate the experimental observations reported previously from the questions addressed by the present simulations.

      In the revised manuscript, we will reorganize the relevant Results subsections so that each section first briefly states the pertinent experimental observation and then defines the specific mechanistic question addressed by the simulations. We will also distinguish more explicitly between trends reproduced by the simulations and hypotheses that emerge uniquely from the simulation analysis. This restructuring should make it clearer which conclusions are supported directly by prior experiments and which represent new mechanistic predictions of the present work.

      The authors directly used Automartini to generate the ligand parameters and then checked the contact fraction between the ligand and the pocket. Such analysis only tells whether the ligand visits the pocket but does not test either specificity or binding affinity, and therefore does not support the claim of ligand pocket recognition. The simulation concentration is also in the millimolar regime, probably much higher than the experimental range, so it's not surprising to see ~10% to 20% contact fraction. Considering that no native interactions are used to restrain the ligand-pocket interactions, I suspect the ligand might also have similar contacts to the other regions of HDAC. The authors might want to be careful about their discussions.

      We agree that observing contacts between the ligands and the catalytic pocket does not by itself establish specific recognition or binding affinity. The current coarse-grained simulations were designed primarily to examine how the presence of the inhibitors perturbs the protein interaction network rather than to calculate ligand-binding affinities.

      In response to this concern, we will analyze ligand interactions with non-pocket regions and compare these with the catalytic-pocket contacts, allowing us to better quantify the degree of preferential localization. We will also validate the physical reasonableness of the coarse-grained ligand parameterization, including the nonbonded parameters relevant to ligand-protein interactions, and report the validation and its limitations more explicitly in the revised manuscript.

      Throughout the manuscript, we will revise language implying that the coarse-grained simulations demonstrate specific ligand recognition or reproduce experimental binding affinities. We will also clarify the significance of the relatively high ligand concentrations used in the simulations and relate these conditions to the experimental concentrations.

      The claim regarding the IDR dimension might be problematic. It's still an open question whether a coarse-grained model, or even current all-atom force fields, can reliably resolve the effect of a single phosphorylation on IDR dimensions. I agree that all current results, including the error estimate, support that Rg do not change that much, but the authors might still want to be careful about such claim considering such small perturbation in the sequence.

      We appreciate this point and agree that the absence of a detectable change in radius of gyration in the present simulations should not be interpreted as evidence that a single phosphorylation cannot alter IDR dimensions more generally.

      We will provide appropriate uncertainty estimates and statistical comparisons for the single-chain conformational ensembles and revise the wording to state more narrowly that, within the resolution of the present Martini 3 simulations, we do not detect a substantial phosphorylation-dependent change in IDR1 dimensions. We will also acknowledge the limitations of current coarse-grained models in resolving subtle conformational effects associated with a single post-translational modification.

      The proposed electrostatic rewiring is based on the difference between the contact maps of WT and phosphorylation variant. It looks like the difference is relatively small to the contact probabilities. There is also no error estimation or pattern validation from multiple simulations. The authors might want to quantify the uncertainties of such patch-level relative changes by either checking some independent simulations or time-block variations.

      We agree that the relative changes visible in the phosphorylation-dependent contact maps need quantitative uncertainty estimates.

      In the revised manuscript, we will quantify the reproducibility of these contact changes using time-block analyses together with additional independent slab simulations, and provide uncertainty estimates for the difference maps. We will use these analyses to identify which phosphorylation-dependent contact changes are robust and will restrict the mechanistic interpretation to features that are reproducibly resolved.

      A similar comment applies to the claims on the charged-contact reorganization mechanism of two small inhibitors on LLLPS. Error estimates should be provided for these contacts when comparing different variant. In cases when errors are large (e.g. contact number with errors reported in Fig. 4K to N), statistical analysis (p-values) is needed to show the significance of the results.

      We agree that the same consideration applies to the comparison of the ligand-dependent interaction networks.

      We will report uncertainty estimates for the relevant contact observables and strengthen the statistical assessment using additional independent slab simulations together with expanded independent sampling of the full-length multichain systems. Statistical comparisons will be based on independent estimates, and where the variability is comparable to or greater than the apparent differences between conditions, we will explicitly report this and avoid assigning mechanistic significance to those differences.

      Regarding the proposed "phospho-S22-centered" contact network, I had some difficulty recognizing the specific regions discussed in Figures 2 and 3. It might be helpful to mark these regions directly on the contact maps. The authors may also wish to consider whether "S22-centered" is the most accurate description, or whether the observed changes are better described as involving a broader segment spanning several residues around S22. And since phosphorylation is introduced at S22, observing changes near S22 is not unexpected and may not be sufficient to establish phospho-S22-centered network.

      We appreciate this suggestion. We agree that describing the observed interaction changes as strictly "S22-centered" may be overly specific, because phosphorylation is introduced at S22 and the altered contacts involve a broader sequence region surrounding this residue.

      We will mark the relevant sequence regions directly on the contact maps to make these patterns easier to identify. We will also reconsider the terminology throughout the manuscript and, where more appropriate, describe the effect as a phosphorylation-responsive interaction region or network surrounding S22 rather than implying that S22 alone constitutes a discrete interaction node.

      Within the last result section, the authors argued that FD3 plays an important role in both phosphorylation- and ligand-regulated HDAC assembly. However, only contact-based analysis instead of saturation concentration was presented. I understand the substantial challenge of obtaining converged saturation concentration for such system with multiple domains, if so, the authors need to discuss such caveats.

      We agree that the full-length simulations provide information about the organization of the assembled state but do not directly determine the saturation concentration of full-length HDAC6. Given the size and complexity of the full-length system, obtaining converged coexistence concentrations would require substantially larger simulations and longer sampling than those performed here.

      We will make this distinction explicit in the revised manuscript and clarify that the full-length contact analysis addresses changes in the internal interaction network of the assembled state rather than directly measuring changes in phase-separation propensity.

      Though direct experimental evidence of such FD3-centered interaction network does not exist, the experimental paper did provide measurements for a couple domain-deletion constructs, including the deletion of the FD3 (BUZ) region. This might be worth discussing, considering the manuscript focuses on the role of FD3 region. In particular, deletion of the BUZ region did not appear to affect LLPS. This does not necessarily exclude a role for FD3 in phosphorylation- or ligand-dependent regulation. However, it may complicate the description of FD3 as the center of the condensate network. The authors might want to discuss how their model can be reconciled with these domain-deletion results and consider their corresponding conclusion.

      We appreciate the reviewer drawing attention to the previously reported BUZ/FD3 deletion experiments. We agree that the observation that deletion of the BUZ region does not abolish LLPS indicates that FD3 is not required for baseline phase separation.

      In the revised manuscript, we will discuss these experiments explicitly and will avoid describing FD3 as an obligatory center of the HDAC6 condensation network. Instead, we will discuss the possibility suggested by the simulations that FD3 contributes to the organization or regulation of interactions within the full-length assembly without being necessary for assembly itself. We will also clarify that this interpretation remains a model-derived hypothesis requiring experimental validation.

      Finally, the authors describe their mechanism as coupled networks. They might want to clarify the coupling they meant. If this refers to the coupling between different regions (e.g. S22 and FD3) or domains, they might want to consider more quantitive analysis, for instance by conditional/correlated contacts between domains. Changes in contact patterns at different conditions do not necessarily show the coupling between domains.

      We agree that condition-dependent changes in two interaction networks are not, by themselves, sufficient to demonstrate coupling between them.

      We will clarify what we mean by coupling and will examine whether conditional or correlated contact analyses in the full-length simulations provide quantitative evidence that changes involving one region or domain are associated with changes elsewhere in the interaction network. If these analyses do not support such a relationship, we will revise the terminology and describe the results more conservatively as coordinated or condition-dependent reorganization of multiple interaction networks.

      Reviewer #3 (Public review):

      (1) There are areas where statistical tests can improve the quantitative presentation of the work. Specifically, the difference in the IDR ensembles for the isolated chains should be assessed for statistical significance.

      We agree that the comparisons of the isolated-chain ensembles should include quantitative estimates of uncertainty and statistical support.

      In the revised manuscript, we will quantify the variability of the relevant conformational observables across independent simulations and perform appropriate statistical comparisons between the unmodified and phosphorylated IDR1 ensembles. We will use these results to refine the statement regarding the effect of phosphorylation on the single-chain dimensions and avoid interpreting differences that are not resolved within the uncertainty of the simulations.

      (2) There appears to be considerable finite-size effects in some of the slab simulations that may affect the conclusions. In particular, the chains along the long axis appear to be interacting across the periodic boundary. This can happen if the slab long axis is not long enough/if the ratio of dilute phase to dense phase is not large enough.

      We appreciate the reviewer raising this important issue. Interactions between the condensed slab and its periodic images could bias estimates of the dilute-phase concentration and therefore need to be explicitly evaluated.

      We will examine the trajectories and density profiles for evidence of interactions across the periodic boundary and quantify the separation between the condensed phase and its periodic images. We will also evaluate the sensitivity of the relevant conclusions to the simulation box dimensions and, where necessary, perform additional checks using enlarged simulation dimensions for representative systems. We will report the outcome of these tests and discuss any remaining finite-size limitations when interpreting the saturation concentrations.

      (3) Some decisions in the simulation conditions require further justification/explanation. More specifically, the concentrations of small molecules used, as well as the decision to conduct simulations at 295 K, should be adequately justified.

      We agree that these simulation conditions require clearer justification.

      The relatively high ligand concentrations were used to increase the number of ligand–protein encounters that can be sampled within the accessible coarse-grained simulation timescales, rather than to quantitatively reproduce the experimental ligand concentrations. In the revised manuscript, we will state this rationale more explicitly, compare the simulated concentrations with the experimental conditions, and discuss the implications of the elevated ligand concentrations for interpreting ligand–protein contact frequencies and possible nonspecific interactions.

      The simulations were conducted at 295 K to match the temperature used in the corresponding experimental measurements that motivated this study. We will clarify this point in the revised manuscript and explicitly state that the simulation temperature was chosen to maintain consistency with the experimental conditions.

      (4) Given the size of the full-length protein, it is understandable that the full dense phase is not probed for the full-length protein. However, it would be useful to comment on how the conclusions may change in the context of the full length. For instance, there is no real interface that the small molecules need to diffuse across and so discussions on the role of partitioning in the paper should be better contextualized in light of this.

      We agree that the full-length simulations should be distinguished more clearly from the slab simulations of IDR1. The full-length systems probe the organization of an assembled high-concentration state and do not contain a sufficiently large coexisting dilute phase and interface from which equilibrium partitioning or a saturation concentration can be determined.

      We will revise the relevant discussion of ligand partitioning to reflect this limitation. Where the current wording implies equilibrium partitioning between dilute and dense phases in the full-length simulations, we will instead describe the measured quantity directly—for example, ligand localization or enrichment within the simulated protein assembly.

      More generally, we will clarify throughout the manuscript which conclusions concern the internal organization of the full-length assembled state and which conclusions concern phase-separation propensity inferred from the IDR1 slab simulations.

    1. eLife Assessment

      The work presented represents important new evidence linking the cascade of neural processes triggered by memory-based prediction errors. The study uses an impressive collection of approaches and methods to characterize and measure cognitive control, arousal, and memory changes as a function of memory-based violations. The analyses are technically sophisticated and rigorous and, taken together, provide solid evidence that there are multiple processes accompanying prediction errors, and that they differentially relate to successful encoding.

    2. Reviewer #1 (Public review):

      This manuscript describes a multi-modal study of associative learning and memory in humans that combines scalp EEG, pupillometry and behavioral analysis to explore the construct of mnemonic prediction errors (MPEs), in terms of their relationship to attention and cognitive control. Across two pooled studies, participants performed associative memory tasks in which they learned the relationship between a cue word (action verb) and subsequent picture (animate or inanimate) with a strong vs. weak (4 or 1 repetitions) encoding manipulation. At test, participants were encouraged to generate a prediction following the cue word to determine whether the subsequently presented picture was a match or mismatch. The timecourse of pupillary responses during match decisions were decomposed using temporal principal components analysis, which identified 6 distinct and overlapping processes. Some of the components (PC3/PC4) exhibited sensitivity to both the strength and mismatch conditions, as well as behavior (both RT and accuracy) and retrieval success on the subsequent trial. Furthermore, relationships were also observed between pupillary responses (specifically for PC4) and both frontal theta and posterior alpha power measures obtained from scalp EEG in Experiment 2, as well as for frontal theta and subsequent learning from mismatch stimuli (assessed using subsequent memory findings from a surprise recognition test). The authors suggest the findings indicate that MPEs elicit changes in attention, arousal and cognitive control which impact subsequent learning.

      Strengths:

      This manuscript has many strengths, including a clever study design, thoughtful integration of multiple neurocognitive measures, and a set of rigorous and technically sophisticated analyses, which reveal a large set of relationships among the measures and behavior. The findings demonstrating brain/physiology-behavior relationships are particularly important, in that they point to potential functional consequences of MPEs.\

    3. Reviewer #2 (Public review):

      Summary:

      The authors studied cognitive control and attention in response to mnemonic prediction errors (MPEs): situations in which the external reality violates internal memory-based predictions. The behavioral task first established strong versus weak predictions, and then either confirmed or violated these predictions. The authors examined markers of cognitive control (frontal theta) and attention (posterior alpha suppression, pupil response) while strong and weak predictions were confirmed or violated. They found increased cognitive control (frontal theta) for strong MPEs, which correlated with subsequent memory. Markers of attention (alpha suppression, pupil response) also accompanied strong MPEs but did not correlate with subsequent memory. Pupil response was investigated using an interesting approach that decomposes the response into different components, finding that different components respond earlier or later and show different correlations with MPEs and their strength. The authors also investigated how EEG, reaction time, and pupil responses correlated with one another, providing further insight into the mechanism underlying the response to MPEs. Together, the study points toward multiple control and attention mechanisms involved in MPE response and memory.

      Strengths:

      The study has a clear behavioral paradigm with multiple measures - behavioral, EEG, and pupillometry that offer an investigation into different aspects of MPE response and memory.

      The study is also very comprehensive in looking at multiple phases in processing MPEs: the prediction phase (prior to the violation), the response to MPEs, and subsequent memory of MPEs, all within one study. Specifically, the link between neural mechanisms and subsequent memory is a major advancement, as most prior studies did not include this component. Mechanisms underlying subsequent memory of MPEs are theoretically important, as a primary function of MPEs is to promote learning and memory. As the authors mention, the different neural and pupillary signals are not robustly correlated, suggesting multiple mechanisms underlying MPE detections, which is interesting, offers avenues for future research, and can facilitate a better theory of how MPEs are processed in the brain. Finally, the decomposition of pupil response into different components and their correlation with behavior (RT during match/MPE detection) is interesting.

    4. Author response:

      The following is the authors’ response to the previous reviews.

      Note from the authors:

      During review of the proofs and re-confirmation of all models and outcomes, we discovered that in the current version of the manuscript, the identity of the lagged time series in the exploratory cross-correlation analysis was misreported. In the cross-correlation plot (Fig. 4d), the posterior alpha time series was fixed and the frontal theta time series was lagged by the times indicated on the x-axis, not vice versa. The inset figures depicting the timeseries will be corrected accordingly in the Version of Record. The subsequent mediation model, in which we tested whether attention mediates the relationship between mnemonic prediction strength and cognitive control, was included as a follow-up to the previously reported cross-correlation outcomes and will therefore be removed. As noted in the Version of Record, the corrected interpretation of the temporal ordering is that in response to a strong mnemonic prediction error, increases in cognitive control precede increases in attention.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This manuscript describes a multi-modal study of associative learning and memory in humans, that combines scalp EEG, pupillometry and behavioral analysis to explore the construct of mnemonic prediction errors (MPEs), in terms of their relationship to attention and cognitive control. Across two pooled studies, participants performed associative memory tasks in which they learned the relationship between a cue word (action verb) and subsequent picture (animate or inanimate) with a strong vs. weak (4 or 1 repetitions) encoding manipulation. At test, participants were encouraged to generate a prediction following the cue word to determine whether the subsequently presented picture was a match or mismatch.

      The timecourse of pupillary responses during match decisions were decomposed using temporal principal components analysis, which identified 6 distinct and overlapping processes. Some of the components (PC3/PC4) exhibited sensitivity to both the strength and mismatch conditions, as well as behavior (both RT and accuracy) and retrieval success on the subsequent trial. Furthermore, relationships were also observed between pupillary responses (specifically for PC4) and both frontal theta and posterior alpha power measures obtained from scalp EEG in Experiment 2, as well as for frontal theta and subsequent learning from mismatch stimuli (assessed using subsequent memory findings from a surprise recognition test). The authors suggest the findings indicate that MPEs elicit changes in attention, arousal and cognitive control which impact subsequent learning.

      Strengths:

      This manuscript has many strengths, including a clever study design, thoughtful integration of multiple neurocognitive measures, and a set of rigorous and technically sophisticated analyses, which reveal a large set of relationships among the measures and behavior. The findings demonstrating brain/physiology-behavior relationships are particularly important, in that they point to potential functional consequences of MPEs.

      Weaknesses:

      The technical proficiency and complexity of the study and analysis also presents a clear limitation and challenge for interpretation. It is likely that readers, even those that are quite knowledgeable about the methods, constructs, and questions being addressed will often struggle (as this reviewer did) to keep the large set of findings in mind and gain understanding of how they all fit together.

      Indeed, it seems like there many threads running together in the paper which make it challenging to find the through-line of the key findings. The authors do address some of the key questions motivating the paper in the Introduction, but the results are somewhat ambiguous with regard to the primary question of the study as to whether the detection of MPEs leads to interaction among cognitive control, attention, and arousal. To their credit, the authors tackle this question through both cross-correlation and formal mediation analyses, and summarize these in diagrammatic figures (Figure 3, Figure 6). Yet it is not resolved whether the results represent a clear answer pointing to independence, or rather a lack of statistical power, or ill-resolved formulation of the mediational relationship. In particular, the cross-correlation suggests that posterior alpha suppression in response to MPEs does precede frontal theta, yet this indirect relationship does not explain the variation in trial-by-trial RTs on mismatches. This suggests a potential model misspecification.

      In addition to the primary interaction issue mentioned above (between cognitive control, attention & arousal), the Introduction lays out a number of claims:

      (1) That pupil size will be more sensitive to strong than weak MPEs.

      (2) That MPE-linked increases in attention (indexed with posterior alpha suppression) and arousal (indexed with pupil size) will be linked to learning.

      (3) That MPE learning will vary as a function of prediction strength.

      Given the focus on learning, it is somewhat surprising that learning is not included in the mediation models. As the authors indicate in the Discussion, the use of trial-by-trial RT variation to drive the mediation model might be problematic, given that the RTs are sensitive to a range of factors beyond mnemonic prediction strength and also are under competing pressures (longer for mismatches than matches, due to surprise-linked slowing, but also faster following stronger rather than weaker mnemonic predictions). Thus, an alternative possibility might be to use trial-by-trial recognition of mismatches as the outcome variable in mediation models rather than trial-by-trial RT as the independent variable.

      A large component of the results (Sections 2 and 3) is devoted to analyses of cue-linked pupil and EEG processes that putatively reflect mnemonic predictions (i.e., occurring before picture probes are presented and match/mismatch detection, i.e., MPEs occur). Yet these Results and the subsequent pupillary PCA components (PC1 and PC5) that are elicited are not well-integrated with the primary themes of the paper or the causal hypotheses. One finding that does seem to figure prominently (in that it is mentioned in Abstract, Introduction & Discussion) relates to the amount of attention allocated to the mnemonic prediction generation. Yet this finding is not well emphasized in the Results themselves. Possibly it refers to the negative relationship between posterior alpha during memory retrieval and the magnitude of pupillary PC3 component, described in Section 3. But it was quite challenging to identify amongst the wealth of results described in this Section as well as the others. More generally, the large amount of findings described across all four lengthy Results sections makes it challenging for readers to discern what are the key ones that the authors would like to highlight.

      It is recommended that the authors do another pass through the paper to better highlight the most critical findings that they want to emphasize or which are most interpretable from a mechanistic and causal flow perspective and then de-emphasize or move other findings to the Supplemental Materials. Although the authors are to be commended for such a rigorous and comprehensive set of analyses, there are so many of them and findings, that the key points get buried and the reader needs to struggle potentially unnecessarily to identify the key take-away points.

      We thank Reviewer 1 for the helpful feedback on how the manuscript can be further strengthened. We recognize that the rich set of findings can overwhelm the reader, resulting in difficulty discerning the main take aways about the effects of mnemonic prediction errors. We particularly appreciate Reviewer 1’s encouragement to restructure the manuscript so as to focus on the findings reported in Sections 1 and 4 of the original revision; the current revision now focuses on these key observations.

      As part of this restructuring, Reviewer 1 also proposed moving the content from Sections 2 and 3 of the original revision to the Supplement. We agree with the Reviewer that the questions addressed in these sections on retrieval-related processes are not the main focus of the paper, but that they are informative in their own right. To avoid their getting lost in the Supplement, we decided that these results would be better served in a separate manuscript and thus we have removed them entirely. 

      We acknowledge in the revised manuscript that the mediation and cross-correlation analyses were exploratory and that these specific analyses may not be well powered in the current experiments. With respect to Reviewer 1’s concerns about the specification of the mediation models, we were motivated to test whether MPEs trigger an increase in cognitive control that in turn, triggers an increase in attention and/or arousal (Fig. 4a); as such, we designed the model to assess whether, on strong MPE trials, frontal theta mediates the relationship between prediction strength and attention/arousal. As noted in the manuscript and raised by Reviewer 1, mismatch RT here is an imperfect measure of trial-level prediction strength. Future experiments that selectively elicit strong MPEs and have a more controlled measure of trial-level prediction strength may be better equipped to address these questions about interactions between control, attention, and arousal. We agree with Reviewer 1 that models assessing subsequent memory as an outcome would be desirable. However, given that (a) we did not find strong evidence for interactions at the time of a strong MPE and (b) we only observed a relationship between frontal theta and subsequent memory (but not posterior alpha or pupil), subsequent memory mediation models do not appear to be well justified. Altogether, these findings illuminate open avenues for future research.

      Reviewer #2 (Public Review):

      Summary:

      The authors studied cognitive control and attention in response to mnemonic prediction errors (MPEs): situations in which the external reality violates internal memory-based predictions. The behavioral task first established strong versus weak predictions, and then either confirmed or violated these predictions. The authors examined markers of cognitive control (frontal theta) and attention (posterior alpha suppression, pupil response) while strong and weak predictions were confirmed or violated. They found increased cognitive control (frontal theta) for strong MPEs, which correlated with subsequent memory. Markers of attention (alpha suppression, pupil response) also accompanied strong MPEs but did not correlate with subsequent memory.

      Pupil response was investigated using an interesting approach that decomposes the response into different components, finding that different components respond earlier or later and show different correlations with MPEs and their strength. The authors also investigated how EEG, reaction time, and pupil responses correlated with one another, providing further insight into the mechanism underlying the response to MPEs. Together, the study points toward multiple control and attention mechanisms involved in MPE response and memory.

      Strengths:

      The study has a clear behavioral paradigm with multiple measures — behavioral, EEG, and pupillometry — that offer an investigation into different aspects of MPE response and memory.

      The study is also very comprehensive in looking at multiple phases in processing MPEs: the prediction phase (prior to the violation), the response to MPEs, and subsequent memory of MPEs, all within one study. Specifically, the link between neural mechanisms and subsequent memory is a major advancement, as most prior studies did not include this component. Mechanisms underlying subsequent memory of MPEs are theoretically important, as a primary function of MPEs is to promote learning and memory. As the authors mention, the different neural and pupillary signals are not robustly correlated, suggesting multiple mechanisms underlying MPE detections, which is interesting, offers avenues for future research, and can facilitate a better theory of how MPEs are processed in the brain. Finally, the decomposition of pupil response into different components and their correlation with behavior (RT during match/MPE detection) is interesting.

      Weaknesses:

      The methods are rigorous, and the data support the claims. The weaknesses are minor and are offered here as avenues for future research.

      (4) The relationships the authors find between brain measures and pupil components were largely not specific to mismatches/matches. Thus, the specificity of this relationship is untested.

      (5) The results with subsequent memory are important and address a major gap in the field that largely did not relate neural effects of MPE to subsequent memory. However, one major limitation of the study is that the authors did not test memory for matches. I understand the logic of avoiding testing matches. Because matches were repeated more times in the study, it’s not a fair comparison and could change participants’ overall criterion for old/new decisions. Future research could address this, e.g., by testing weak matches or potentially using a between-subject design.

      We appreciate Reviewer 2’s helpful feedback during the review process and encouraging comments on the strengths of the manuscript. We agree and note in the revision that it would be illuminating for future studies to contrast memory for events that violate and confirm mnemonic predictions.

      Comments on revised version

      The authors addressed all my concerns. I appreciate the authors’ thoughtful and detailed response.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      It was challenging to read the paper with key findings happening at the time of the MPE presented first, and then to go “backwards in time” to examine process that occurred at preceding time periods (i.e., prior to probe presentation), and then again forward in time to examine learning related processes.

      (1) Restructuring the Results

      In this regard two distinct recommendations are made:

      - Move Sections 2 and 3 to Supplemental Materials, to maintain the focus on the key findings related to detection of MPEs and their effects on subsequent learning.

      - An alternative structure would be to present the cue-locked findings first, which relate to mnemonic predictions, then to those that occur when those predictions get violated, and finally to the learning process that occur following MPEs and which can be detected with subsequent recognition tests.

      We thank the Reviewer for this encouragement to restructure the manuscript. To address concerns regarding the density of the paper and the cohesiveness of the findings, we removed the content that was in Sections 2 and 3 of the original revision. We will publish those results in a separate paper with additional analyses to more directly address previously raised questions regarding the mechanisms indexed by frontal theta and posterior alpha at the time of retrieval.  

      (2) Integration of the summary figures

      At the minimum, a recommendation would be to better integrate Figure 6, and maybe various versions of Figure 3, earlier into the text, preferably even in the Introduction, and then repeatedly refer to them throughout the results. For example, in Figure 6, linking the leftmost panel of the figure to Sections 2 and 3 is critical, and the righthand panel to Sections 1 with the rightmost part related to learning explicitly linked to Section 4.

      We moved the summary figure up to now be Figure 1; we reference this figure in the Introduction and throughout the manuscript; and we additionally make reference in the figure to the association between frontal theta and PC3 at the time of a strong MPE as well as the cross-correlation outcome. We hope these modifications further aid the reader in identifying the main findings of the manuscript.

      (3) Specification of the mediation models

      Additionally, for the mediation models it is quite unclear why mismatch RT is treated as the index of MPE magnitude, as this seems to be where the problem may lie in model fitting. Why not think of this as an outcome variable (since would seem to be a causal outcome of the underlying functional processes elicited by mismatch detection)?

      We thank the reviewer for these thoughtful comments. Our goal in designing the mediation models in Fig. 4a-c was to test our hypothesis that strong MPEs trigger an increase in cognitive control that, in turn, triggers an increase in attention and/or arousal; thus, attention/arousal should be the outcome and cognitive control should be the mediator. Given that increases in attention and cognitive control were selectively observed for strong and not weak MPEs, the models were restricted to strong MPEs. We therefore needed a trial-level measure of MPE magnitude to determine whether stronger MPEs in the strong condition elicit greater attention/arousal by engaging more cognitive control. As such, we decided to use mismatch RT as a proxy measure of MPE magnitude; we acknowledge in the text that this measure is imperfect. While a model with mismatch RT as the outcome variable is possible, the relative timing of the attention/arousal effects (which largely occur following responses) would render interpretation to be more challenging. Had stronger evidence of indirect effects emerged in Figs. 4b-c, we could have conducted model comparison with RT as an outcome. We acknowledge in the text that these analyses were exploratory and characterization of potential indirect effects will require more data and a more precise measure of MPE magnitude.

      Alternatively, examining trial-by-trial recognition memory of mismatches as the relevant outcome variable would also seem to capture the functional process of interest. In this regard, have the authors examined whether trial-by-trial RT on mismatches predicts subsequent recognition of these items? If this direct relationship holds, it could be a target for mediation analyses in itself.

      We agree with the reviewer that in theory, a mediation model predicting subsequent memory would be a desirable test of an integrated model of the mechanisms underlying MPE-driven learning. However, the mediation analyses conducted to address the functional relationships at the time of a prediction error (Fig. 4) are not well-powered to begin with; this limitation is raised in the Results and the Discussion. A mediation model predicting subsequent memory would be similarly underpowered; given that there is not strong evidence for indirect effects at the time of a strong MPE, and that only frontal theta – and not posterior alpha nor pupil – predicts subsequent memory, we think that such a mediation model is not well justified to include. Such a model should be more directly tested by well-powered designs in future studies.

      (4) Hippocampal theta in the Introduction

      The Introduction discusses hippocampal theta as well as frontal theta yet also makes clear that the former is not really well-detected or analyzed using scalp EEG. Consequently, a recommendation would be to remove this paragraph from the Introduction, since it can be misleading and a “red herring” for the reader, and instead only bring up this point in the Discussion section, as a pointer to the need for future research using methods that may be more sensitive to hippocampal interactions with PFC regions.

      We appreciate this point and moved discussion of the hippocampus from the Introduction to the Discussion.

    1. eLife Assessment

      This study provides valuable new insights into the functional differentiation of human amygdala subnuclei during the observation of negative images and emotional reappraisal. The evidence is convincing, particularly regarding differences in the time course of evoked responses and their partial correspondence with broad anatomical subdivisions. The work will be of interest to researchers studying the amygdala, emotional processing, and emotion regulation.

    2. Reviewer #1 (Public review):

      Summary:

      The authors aimed to re-analyse two fMRI datasets of studies investigating the effect of cognitive emotional regulation on amygdala response during the viewing of negative images, in order to assess whether such effects might have been missed in previous reports of the results of these studies. In order to accommodate potential non-typical response profiles in the amygdala, they used a modelling approach that is agnostic about the shape of the BOLD response (finite impulse response modelling). Furthermore, the authors sought to differentiate between amygdala subregions using i) two previously published probabilistic atlases of amygdala subnuclei; and ii) a data-driven analysis focusing on temporal response profiles, which identified four clusters that mostly overlapped with anatomical parcellations. The analyses confirmed their previous results insofar as they did not find any impact of emotional regulation on the amygdala response in any subregion. However, their approach revealed differences in temporal response profiles between amygdala subregions: the centromedial amygdala showed sustained activation extending into the post-stimulus rating period, whereas laterobasal and superficial subregions peaked earlier during stimulus presentation. Overall, this study helps to clarify whether cognitive emotional regulation during negative image viewing alters amygdala response, and demonstrates the value of flexible response modelling to differentiate between functional subregions of an important brain structure.

      Strengths:

      The strengths of this study lie in the amount of data used (a combined N of 358 participants is rather high), the flexibility of the modelling approach (FIR), the exploratory nature of the data-driven clustering based on temporal response profiles, and clarity of the text and of the findings. One weakness would be the fact that no fMRI data acquired at higher field strengths were used for the aim of differentiating responses of amygdala subunits.

      In my opinion, the authors achieved their aims of re-analysing these datasets with complementary methods, and helped to discover new aspects of the BOLD response in the amygdala during emotional regulation. The results support their conclusions.

      This work is likely to stimulate discussion of the response of amygdala subnuclei during emotional regulation, and might encourage researchers to resort to more flexible analysis methods by demonstrating the information gain achieved with the FIR approach.

      Weaknesses:

      There are 7T studies of the amygdala response that have looked at differences in BOLD response across subnuclei. While I don't know of any that have investigated emotional regulation, I think these studies should nevertheless be mentioned and discussed in the Discussion, at least to compare the differences observed across subnuclei.

    3. Reviewer #2 (Public review):

      Summary:

      In this paper, Bo et al. study the spatiotemporal organization of BOLD responses in the amygdala. They apply FIR modelling to a large sample (two cohorts tested at the same site and scanner, totalling >350 individuals) who completed a negative/neutral picture viewing and cognitive reappraisal task. Averaging voxelwise BOLD responses across two distinct probabilistic atlases of the amygdala, the authors found that a corresponding centromedial region showed sustained activation to negative stimuli beyond the stimulus period. Laterobasal and superficial subregions peaked earlier, within the stimulus presentation period. In all cases, reappraisal did not change these profiles. Lateralization effects (left-side dominance) were found in the CM region at later time points, and to a lesser extent in the superficial nucleus area at earlier time points. Data-driven clustering of voxelwise time courses could partly recover findings from probabilistic atlases, but nonetheless showed partial decoupling of anatomical and functional boundaries in the amygdala.

      Strengths:

      This is a well-written paper with a clear objective addressed by sound methods. The authors set out to address two main questions: (1) "do amygdala subregions exhibit distinct temporal dynamics in response to negative emotional stimuli?" and (2) "does cognitive reappraisal differentially modulate these temporal profiles across subregions?" Regarding question 1, FIR modelling results indicate strong differentiation of the centromedial region of the amygdala from other subdivisions, with strong and late responses to presented negative stimuli. On question 2, cognitive reappraisal does not seem to play a major role here, although I raise a question on this in the next section. Overall, all presented conclusions are supported by study findings. I would add that lateralization effects are not mentioned in the main objectives/questions of the paper, but this analysis represents a large section of the results that may warrant mention in the introduction as well.

      Weaknesses:

      (1) An important concern when looking at mesiotemporal regions such as the amygdala is signal-to-noise. Yet, this challenge in analyzing data from the amygdala was not addressed here but represents a potential confound if SNR varies within the amygdala along its major subdivisions.

      (2) The authors analyze BOLD evoked response time courses, but I did not find a GLM analysis showing that the task reliably engaged the amygdala, either when analyzed with a canonical HRF or with the FIR model used here. We can see from Figure 2 that the task may not engage the entire amygdala (e.g. LB/LA subregions seem to show lower amplitude evoked-responses), yet the authors focus equally on time courses of subregions engaged and not engaged in the task. As such, differences observed between subregions may reflect different underlying processes (e.g. different processes of engagement in the task, or high/low engagement).

      (3) The study uses two different probabilistic atlases to parse BOLD response time courses in the amygdala. However, methodological details on the processing of the atlas data are missing from the text. For example, it is unclear if the subregions were thresholded or overlap in any way, which would affect the strength of detected effects if there is substantial overlap between areas.

      (4) The authors found no effect of reappraisal on BOLD response time courses. However, if I understand correctly, not all participants showed successful reappraisal according to their behavioural ratings (lines 125-126: "Eighty-one percent of participants showed a regulation effect in the same direction as the group-level effect"). Were these participants excluded from the reappraisal analysis on evoked response time courses? This is currently unclear, but holds significance in reliably answering question 2.

    1. eLife Assessment

      This study provides important novel insight into how Wnt/beta-catenin signaling may regulate the responsiveness of blood vessels in the brain and retina to vascular endothelial growth factor, with potential relevance to Wnt surrogate therapies aimed at restoring vascular barriers. Solid evidence that Wnt/beta-catenin signaling changes the protein abundance and relative balance of two vascular endothelial growth factor receptors in brain blood vessels is provided by informative genetic mosaic experiments; in the retina, however, regulation of VEGFR2 was difficult to establish because expression by neighboring cells obscured its detection in blood vessels. Physiological implications are discussed, but evidence remains incomplete because the observed changes in receptor abundance were not directly linked to altered signaling or vascular function in living animals. A second mechanism, in which Wnt/beta-catenin signaling suppresses signaling through VEGFR2 with little or no change in receptor abundance, was inferred from reporter experiments in cell-based systems.

    2. Reviewer #1 (Public review):

      Summary:

      Wang et al. report that changes in the levels of canonical Wnt signaling in CNS ECs modulate protein expression levels of VEGFR2 and VEGFR1. An increase in Wnt/beta-catenin signaling results in a decrease in VEGFR2 and an increase in the decoy receptor VEGFR1; conversely, reduction of Wnt signaling leads to the opposite changes in VEGF receptors in brain CVOs (less clear in the retina). Next to the solid quantitative immunostaining of VEGFR2 and VEGFR1 in multiple CNS regions using genetic mosaics and loss-of-function mouse mutants, the authors employ HEK293T cells to investigate the effect of activation of Wnt/beta-catenin signaling on multiple RTK signaling systems and on the level of overexpressed VEGFR2. The principal conclusions are that Wnt/beta-catenin regulates VEGF signaling. Two distinct levels of regulation are reported: one acts in vivo on receptor protein expression levels, the other acts in 293T cells, and the precise mechanism remains to be identified.

      Strengths:

      Strengths of the study include the use of genetic mosaics that allow for quantification of immuno-staining intensity with an internal reference and the significance of understanding the interplay of beta-catenin-dependent and VEGF pathways.

      There are also some weaknesses noted:

      (1) Experiments to restore the VEGFR2/VEGFR1 ratio in vivo, or to modulate VEGF signaling in vivo, and assess to what extent Wnt/beta-catenin LOF phenotypes can be restored by modulation of VEGF signaling have not been conducted at this point. In addition, it remains to be determined to what degree an experimental recreation of the aberrant VEGFR2/VEGFR1 ratio is sufficient to recreate (partially) some of the phenotypes observed with loss of Wnt/beta-catenin signaling in ECs.

      (2) It would be useful to consider and discuss other essential components of the VEGF signaling system, especially Neuropilins. Wang et al. (2012, Figure 2, PMID: 23217714) reported that CNS ECs with loss of Norrin signaling gain Sema3F binding while not displaying VEGF-B binding sites, suggesting changes in VEGF/semaphorin co-receptor biology.

      (3) Martowicz et al. (2019) PMID: 31533473  also investigated the influence of Wnt/beta-catenin signaling in CNS ECs on VEGFR2 and VEGFR3 expression and reported findings regarding VEGFR2 regulation that appear to differ from the present study. This should be discussed.

      (4) Norrin/beta-catenin and Wnt/beta-catenin signaling have a critical role in CNS angiogenesis, and Wnt surrogates counteract neovascularization in the oxygen-induced retinopathy model. These findings highlight context-dependent roles of Wnt/beta-catenin signaling in normal and pathological ECs. How the modulation of VEGFR2/VEGFR1 may influence developmental and pathological angiogenesis could be discussed.

      (5) When looking up the publicly available scRNA-seq datasets from Heng et al. (2019, PMID: 30988181) from Norrin KO mice, it appears that there is transcriptional regulation of VEGFR1/VEGFR2 consistent with the protein regulation reported here. If transcriptional regulation is important, CMV-driven VEGFR2 expression in Fig.5 supplement 2 may not model the regulatory mechanisms observed in vivo. Does CHIR99021 modulate VEGFR2/VEGFR1 transcripts and proteins in HRMVECs?

    3. Reviewer #2 (Public review):

      The manuscript by Wang, Nathans, and colleagues presents a compelling investigation into the regulatory crosstalk between Wnt/beta-catenin and VEGF signaling within central nervous system (CNS) endothelial cells. Overall, it is a very solid paper from a very respected research lab. The use of in vivo genetic mosaics to directly compare endothelial cells (ECs) with differing beta-catenin states within the same tissue is a particularly elegant approach to mapping receptor dynamics.

      Major Comments:

      (1) Distinguishing Receptor Abundance from Functional Signaling:

      The study relies heavily on immunohistochemistry to assess total VEGFR1 and VEGFR2 levels, inferring that changes in receptor abundance directly equate to altered VEGF signaling. Because immunofluorescence does not establish plasma membrane localization, receptor phosphorylation, or downstream pathway activation, the definitive conclusion that beta-catenin "acts at the receptor level to reduce VEGF signaling" is currently overstated.

      Recommendation: The authors should tone down the functional claims throughout the text (e.g., state that the receptor changes are "predicted to reduce VEGF signaling") or provide direct functional readouts of VEGFR2 activation in vivo, such as phospho-VEGFR2, phospho-PLCγ, ERK, or AKT.

      (2) Limitations of the In Vitro HEK293T Model:

      The HEK293T STF reporter system is utilized to argue for a "post-receptor physiological suppression" of VEGF signaling. While experimentally practical, this non-endothelial system does not definitively establish a physiological mechanism for CNS ECs. Furthermore, NFAT-luciferase is not uniquely specific to VEGFR2 and can be activated by broader calcium/calcineurin pathways. The authors also show that high Wnt1 expression reduces total VEGFR2 protein in these cells, but only at plasmid concentrations much higher than those required to suppress the VEGFR2/NFAT reporter.

      Recommendation: Explicitly decouple the in vitro observations from the physiological in vivo claims. Remove the term "physiological" unless you validate post-receptor suppression in a primary endothelial system under endogenous signaling conditions.

      (3) Regional Heterogeneity (Brain vs. Retina):

      The retinal vascular mosaic data reveal that while VEGFR1 is reduced in BRB- ECs, VEGFR2 shows no obvious difference. The authors correctly acknowledge that Müller glial VEGFR2 expression complicates endothelial quantification, rendering Figure 4C a negative result with uncertain sensitivity. Consequently, the broad generalization that an "increase in Wnt/beta-catenin signaling produces an increase in VEGFR1 levels and a decrease in VEGFR2 levels" across all CNS ECs is not entirely accurate.

      Recommendation: Embrace this biological nuance. Revise the generalized claims to reflect that beta-catenin regulates VEGFR1 consistently across the brain and retina, but VEGFR2 regulation appears context-dependent and is not clearly demonstrated in the retina. To resolve the Müller glia interference, consider orthogonal approaches to isolate the EC signal, such as EC-specific RNAscope or flow cytometry on FACS-sorted ECs.

      (4) Statistical Rigor and Experimental Units:

      The figure legends (e.g., Figure 1) explicitly state that data points represent the mean values from single confocal images. If multiple images were obtained from the same animal, treating them as independent units. Furthermore, the authors utilized unpaired statistics (Wilcoxon rank-sum) to compare BBB+ and BBB- ECs within the same mosaic tissue sections.

      Recommendation: Reanalyze the data using the animal as the primary biological replicate if such data are available (averaging all image measurements within each animal). For mosaic comparisons within the same tissue image/animal, utilize paired statistical tests (e.g., paired t-test or paired Wilcoxon signed-rank test). Figure legends should clearly report the exact N (animals) and n (images/cells), the specific statistical tests used, and confidence intervals.

    4. Reviewer #3 (Public review):

      Summary:

      The manuscript by Wang et al. describes the identification of a novel mechanism by which the canonical Wnt/beta-catenin pathway could regulate endothelial permeability in the central nervous system (CNS). Specifically, they show that in circumventricular organs (CVOs), in which the blood-brain barrier (BBB) is not established in vessels and endothelial cells are highly permeable, the previously reported low levels of Wnt/beta-catenin coincide with high levels of the vascular endothelial growth factor receptor 2 (VEGFR2, Flk-1), whereas the decoy receptor VEGFR1 (flt-1) is expressed at low levels. In order to test the hypothesis in vivo that the Wnt/beta-catenin pathway controls VEGF signaling in CNS vessels by regulating VEGF receptors, the authors analyzed mosaic cerebellar vessels of mice deficient for the Wnt receptor frizzled 4 (Fzd4), showing that BBB+ (normal condition) vessels show low VEGFR2 and high VEGFR1, whereas BBB- (Fzd4ko) vessels show high VEGFR2 and low VEGFR1. Similarly, although not as conclusively due to strong background from Müller cells, these effects were also visible in the retina of these mice. Conversely, dominant genetic activation of beta-catenin signaling in endothelial cells in vivo resulted in upregulation of VEGFR1 and downregulation of VEGFR2 in vessels of the CVOs as well as of the choroid plexus.

      In order to better understand the direct relevance of Wnt/beta-catenin signaling for this regulatory theme, the authors activated the pathway in HEK293 cells, stably harboring the Super-Top-FLASH reporter construct in the presence of VEGFR2 and VEGF. Moreover, by transfecting Wnt1 into HEK293 cells, the authors analysed the effect of TGFbeta, EGF, and FGF signaling. Together, these experiments showed that Wnt/beta-catenin signaling potently suppressed VEGF signaling at low concentrations of Wnt1 transfection, whereas VEGFR2 suppression was only visible at relatively high concentrations. Also, the other pathways were suppressed, although to a lower extent.

      In conclusion, the authors suggest a mechanism of VEGF signaling suppression by Wnt/beta-catenin 1) at the post-receptor level of VEGF receptors and 2) at the transcriptional level of VEGFR1 and VEGFR2, independent of VEGF signaling suppression.

      Strengths:

      (1) The present manuscript provides interesting additional information on top of the published work on the mechanistic function of vascular permeability control by Wnt/beta-catenin, specifically by the highlighted interaction with the VEGF pathway and the opposed regulation of the two VEGF receptors VEGFR1 and VEGFR2.

      (2) They have used state-of-the-art animal models, high-resolution imaging, and appropriate in vitro analysis.

      (3) This finding has potentially strong implications for future therapeutic strategies, aiming at Wnt/beta-catenin activation in various diseases of the CNS in which vascular repair is desired.

      Weaknesses:

      A detailed analysis of the post-receptor and transcriptional mechanisms remains largely unexplored and needs to be explored in the future.

    1. eLife assessment

      This study provides a useful inventory of genes that are up- or down-regulated during the early metamorphic development of male and female larvae and proposes that the microRNA cluster miR-277/34 is involved in the development of sexual differences during early metamorphosis of Drosophila melanogaster, although its precise role remains unclear. The strength of evidence, based on a combination of diverse methods including mRNA and small RNA sequencing, in silico analyses, in vitro assays, and loss-of-function experiments, is incomplete as it lacks a general model and an examination of the potential effects of the miR-277/34 mutations on phenotypes such as morphology or developmental time. This work will be of interest to developmental biologists interested in sexual dimorphism and in the interplay between hormones and microRNAs during development.

    2. Reviewer #1 (Public Review):

      Summary:

      In this paper, Li and colleagues have found mircoRNAs that affect levels of metamorphosis-regulating genes that can also affect levels of sesquiterpenoids (juvenile hormone and related compounds) and ecdysteriods, which regulate the timing and stages of insects, respectively. They first compared the transcriptomes of Drosophila at the third larval instar and at the white pre-pupa stage. They found thousands of differences in gene transcript levels between males and females, and between the two different stages. Among those genes that were differentially regulated they saw that genes involved in insect hormone biosynthesis were disproportionately represented. Many of the differentially regulated genes were involved in the insect hormone biosynthesis pathway and ascorbate and alderete metabolism. MicroRNAs were also differentially expressed during metamorphosis and were separately identified. The authors then considered genes and whether the differentially expressed microRNAs might regulate transcripts known to be involved in sesquiterpenoid production. In silico analysis of microRNAs predicted a list of 17 microRNAs that can regulate transcripts of sesquiterpenoid biosynthesis genes. The authors then used an in vitro luciferase assay to validate the binding and downregulation of 10 of the microRNAs to genes involved with sesquiterpenoid production in S2 cells.

      Li and colleagues then focus on two genes they found were bound by microRNAs that have established roles in metamorphosis. The microRNAs miR-34 and miR-277 bind transcripts of two protein-coding genes that regulate metamorphosis Kr-h1, which encodes a transcription factor that is a JH-inducible transcription factor, and Allatostatin C Receptor 1, (AstC-R1), a G-protein coupled receptor that regulates the corpora allatum, the gland that produces sesquiterpenoids. Using a LAMP assay, one of the microRNAs, miR-277 was shown to bind to both AstC-R1 and Kr-h1 in in vivo whole-animal extracts. There is no mention of binding between either protein-coding transcript and the miR-34 microRNA. Temporal expression of all four transcripts shows that their abundance is anti-correlated; stages of high miR-34 or miR-277 expression correlate with low AstC-R1 or Kr-h1 expression. Homozygous deletions of both mircroRNAs result in 23% lethality, five days after adult eclosion. The authors also generated specific mutants in miR-34 or miR-277 and find differences in the expression of AstC-R1 and Kr-h1 and sex-specific differences in both sesquiterpenoids and ecdysteroids in the knock-out lines. If there were phenotypes associated with the specific knock-outs, those were not mentioned. Next, the authors examined the transcriptomes of the miR-3277 and miR-34 mutants and found several other GO-terms enriched among the differentially expressed genes. However, the sesquiterpenoid pathway and ascorbate and alderete metabolism are not listed.

      Strengths:

      This is an interesting manuscript that could make an important contribution to our understanding of the roles of micro RNAs at metamorphosis, and potentially of how sex-specific differences arise during metamorphosis. Strengths of the paper include the functional validation of microRNA binding, in vitro and in vivo-, as well as the characterization of sesquiterpenoid and ecdysteroid titers. The authors have also used CRISPR to generate specific knock-outs of miR-34 and miR-277. The transcriptomes will be a resource for future work to mine for differences in gene expression during metamorphosis.

      Weaknesses:

      (1) Spatial Expression of miR-34 and miR-277. If miR-34 and miR-277 regulate AstC-R1 and Kr-h1, then they must be expressed in the same cells. Although the authors show that the microRNAs do bind to the transcripts of AstC-R1 and Kr-h1 in S2 cells, and miR-277 binds AstC-R1 and Kr-h1 in vivo whole-animal homogenates, we do not know if the microRNAs are ever in the cells where AstC-R1 or Kr-h1 are expressed. AstC-R1 is only expressed in a few cells in the brain, so it is not at all certain that it is co-expressed with either microRNA. The creation of enhancer lines or in situ hybridization in Drosophila is straightforward and would sort this out.

      (2) Phenotypes. Although a double deletion was used and specific knock-outs of both miR-34 and miR-277 were generated, the analysis of the mutants is very superficial. For the homozygous deletion of both microRNAs miR-34 and miR-277, only a decrease in survivorship was observed a full six days after adult eclosion - after the end of metamorphosis. No phenotype for either miR-34KO or miR-277-KO was given. The authors cite the work of others who have found specific phenotypes after manipulation of sesquiterpenoids or ecdysteroids, like Riddiford and Ashburner, but do not use any of these many studies to help them characterize the phenotype. If the loss of miR-34 and miR-277 affects so many pathways (including MAPK signaling, TGF-beta signaling, FoxO signaling, and Wnt signaling), as well as global titers of metamorphic hormones, then there shouldn't there be something different in the development to discuss?

      (3) I think the reliance on GO term enrichment is getting in the way of biology. For instance, I would not describe Kr-h1 as a sesquiterpenoid biosynthesis pathway gene. Yet the authors say they were motivated to examine microRNA regulation of Kr-h1 because they saw differences in levels of the sesquiterpenoid biosynthesis pathway between WL3 and WPP, a period which also saw differences in expression of some microRNAs. I understand that Kr-h1 expression is regulated by JH, a sesquiterpenoid, but it is not directly involved with JH production, so relying on GO term enrichment has made the decision to focus on Kr-h1 feel arbitrary.

      (4) The transcriptomes of miR-34 and miR-277 should have revealed genes encoding members of the sesquiterpenoid biosynthesis pathway as well as AstC-R1 and Kr-h1, but neither was mentioned. The functional tests of miR-34 and miR-277 were performed because they were shown to affect the levels of expression of genes in the sesquiterpenoid biosynthesis pathway. Figure 2 shows a significant decrease in AstC-R1 and Kr-h1 transcripts after the loss of miR-34 and miR-277. However, the results do not mention either (Lines 250-264). Instead, there is a list of 10 different GO terms (like arginine and proline metabolism or fatty acid degradation) that were enriched in miR-34 and miR-277 transcriptomes. If any of those ten types have any relationship to Kr-h1, AstC-R1, or metamorphosis, that has not been explained.

      (5) Not enough care was taken in describing the stages. The methods describe wandering larvae (WL3) and white pre-pupa (WPP) for the transcriptomes, but in the text, different terms are used, like "larva", "pupa" and "L3 larvae instars" "early pupae" "late L3". Also, it seems like the small RNA libraries for sequencing were taken from "L3 larvae", but the stage of the L3 larvae was not mentioned. Staging is important, especially during metamorphosis, since differences in expression are expected to exist between different stages of L3, between early vs late wandering, and between WPP and early pupal stages.

    3. Reviewer #2 (Public Review):

      Summary:

      This study proposes that the microRNA cluster miR-277/34 controls the generation of sexual dimorphism in Drosophila melanogaster during metamorphosis by acting on specific hormonal and developmental gene pathways.

      Strengths:

      Using a combination of mRNA and small RNA sequencing together with genome-wide in silico and in vitro analyses the authors identified a microRNA cluster that may be involved in metamorphosis and the generation of sexual dimorphism in Drosophila melanogaster.

      Weaknesses:

      Biological validation of the identified sexually dimorphic genes and a detailed understanding of how the microRNA cluster miR-277/34 might be involved in the regulation of sesquiterpenoids are needed.

      Major suggestions:

      (1) If AstC-R1 and Kr-h1 are targets of the miR-277/34 cluster and cause their downregulation, it is not clear why there would also be a decrease in the levels of these genes in the miR-277/34 mutants. This would suggest that the mechanism is not straightforward and that further epistatic experiments should be carried out in order to clarify this issue.

      (2) The changes in the expression levels of AstC-R1 in pupae of miR-277-KO and mir-34-KO flies must be accompanied by photos of the respective larvae and pupae, as well as an analysis of the larvae-pupa transition on the mutants by gender.

      (3) Biological validation of the identified sexually dimorphic genes in vivo will be necessary for the support of this work.

    4. Reviewer #3 (Public Review):

      Summary:

      The authors show convincingly the complexity of gene up- and down-regulation at the outset of metamorphosis and identify substantial differences between the two sexes, even at this early time in development. The complexity of microRNA expression and the difference between the sexes are also nicely laid out. The functional significance of these differences, though, is harder to establish. The authors have focused on the roles of two families of developmental hormones, the ecdysteroids, and the juvenile hormones. The emergence of sex-specific differentiation of organs during metamorphosis is clearly downstream of the action of ecdysteroids and/or JH, but there is no evidence that the presence or lack of these hormones has any effect on the sexual identity of organ systems - i.e., that manipulations of JH or ecdysteroid result in either the masculinization or feminization of individuals or their organs. The precedence for the linkage of these hormones to sex determination is the 2002, Belgacem & Martin study, which describes the effects of JH on fly locomotion. These authors show that the number of stop/start bouts is sexually dimorphic, and removal of JH in males shifts their frequency into the female range while giving treated males exogenous JH moves it back. While this is referred to as a "feminization" of male behavior, this quantitative shift in frequency is not as compelling as would be a qualitative shift -- for example, the removal of JH causing males to show egg-laying behavior (a result that has never been seen). Also, these effects are in a fully mature system, rather than at the early metamorphic time examined in the present paper. In driving and coordination metamorphosis, JH and ecdysteroids are intimately involved in sexual differentiation, but I know of no compelling evidence that they play a role in sex determination.

      While the summary of the effects or removal of specific microRNAs on the components of the biosynthetic pathway for the JHs and ecdysteroids (Figure 2E ,F) is quite compelling, I am concerned about the effects of the removal of mir-277 and mir-34 on the levels of both the JHs and 20E. My concern centers around the data from the control group (w[1118] animals in Figure 2D). These data are the first report of a marked sex difference in the titer of either JH or ecdysteroid at the start of metamorphosis in Drosophila. As expected, males show a 10-20 increase in levels of JH III, JHB3, and 20E between the L3 stage and the white puparium, but, surprisingly, the levels of these hormones in female L3 larvae are equal to or greater than that seen at pupariation! These data for females run counter to over 50 years of work on the effects of ecdysteroids in Drosophila!

      As far as I can gather from the paper, the L3 data were obtained using wandering larvae. This stage lasts for about 12 hours and ends with pupariation. Larvae from this period need to be used with caution for hormone studies. Levels of both JH and ecdysteroid are low as larvae leave the food but rapidly rise to their peak levels at the white puparium stage 12 hours later. To deal with the rapidly changing hormonal landscape through this period, the researchers have used physiological markers to track this progression. Initially, it was the sage of "puffing" of the giant salivary gland chromosomes, but, for bulk collection of staged larvae, larvae are fed on food containing a blue dye, and progression is tracked by the loss of blue coloring from the gut. I could not find if the authors had any criteria for selecting larvae during the wandering period. Male and female larvae grow to different sizes. Might this difference in growth be biased when larvae were selected during their wandering phase?

      The other hormone-related issue is the expression of Kr-h1 during larval stages and metamorphosis (Figure 1G). Kr-h1 is the main target of JH and Kr-h1 expression is often used as a proxy for the JH titer. The authors report that peak Kr-h1 expression occurs in the L3 (when the JH titer should be lowest!) and that it drops at wandering. This pattern is counter to that reported in the literature (e.g., FlyBase, ModEncode).

    5. Author response:

      [Editors’ note: An Expression of Concern has been published for this article. The authors’ responses submitted during revision have been appended for further context]

      eLife Assessment

      This study provides a useful inventory of genes that are up- or down-regulated during the early metamorphic development of male and female larvae and proposes that the microRNA cluster miR-277/34 is involved in the development of sexual differences during early metamorphosis of Drosophila melanogaster, although its precise role remains unclear. The strength of evidence, based on a combination of diverse methods including mRNA and small RNA sequencing, in silico analyses, in vitro assays, and loss-of-function experiments, is incomplete as it lacks a general model and an examination of the potential effects of the miR-277/34 mutations on phenotypes such as morphology or developmental time. This work will be of interest to developmental biologists interested in sexual dimorphism and in the interplay between hormones and microRNAs during development.

      We thank the editor and reviewers for the positive and constructive comments and suggestions. Point-to-point responses are listed below.

      Reviewer #1:

      Summary:

      In this paper, Li and colleagues have found mircoRNAs that affect levels of metamorphosis-regulating genes that can also affect levels of sesquiterpenoids (juvenile hormone and related compounds) and ecdysteriods, which regulate the timing and stages of insects, respectively. They first compared the transcriptomes of Drosophila at the third larval instar and at the white pre-pupa stage. They found thousands of differences in gene transcript levels between males and females, and between the two different stages. Among those genes that were differentially regulated they saw that genes involved in insect hormone biosynthesis were disproportionately represented. Many of the differentially regulated genes were involved in the insect hormone biosynthesis pathway and ascorbate and alderete metabolism. MicroRNAs were also differentially expressed during metamorphosis and were separately identified. The authors then considered genes and whether the differentially expressed microRNAs might regulate transcripts known to be involved in sesquiterpenoid production. In silico analysis of microRNAs predicted a list of 17 microRNAs that can regulate transcripts of sesquiterpenoid biosynthesis genes. The authors then used an in vitro luciferase assay to validate the binding and downregulation of 10 of the microRNAs to genes involved with sesquiterpenoid production in S2 cells.

      Li and colleagues then focus on two genes they found were bound by microRNAs that have established roles in metamorphosis. The microRNAs miR-34 and miR-277 bind transcripts of two protein-coding genes that regulate metamorphosis Kr-h1, which encodes a transcription factor that is a JH-inducible transcription factor, and Allatostatin C Receptor 1, (AstC-R1), a G-protein coupled receptor that regulates the corpora allatum, the gland that produces sesquiterpenoids. Using a LAMP assay, one of the microRNAs, miR-277 was shown to bind to both AstC-R1 and Kr-h1 in in vivo whole-animal extracts. There is no mention of binding between either protein-coding transcript and the miR-34 microRNA. Temporal expression of all four transcripts shows that their abundance is anti-correlated; stages of high miR-34 or miR-277 expression correlate with low AstC-R1 or Kr-h1 expression. Homozygous deletions of both mircroRNAs result in 23% lethality, five days after adult eclosion. The authors also generated specific mutants in miR-34 or miR-277 and find differences in the expression of AstC-R1 and Kr-h1 and sex-specific differences in both sesquiterpenoids and ecdysteroids in the knock-out lines. If there were phenotypes associated with the specific knock-outs, those were not mentioned. Next, the authors examined the transcriptomes of the miR-3277 and miR-34 mutants and found several other GO-terms enriched among the differentially expressed genes. However, the sesquiterpenoid pathway and ascorbate and alderete metabolism are not listed.

      Strengths:

      This is an interesting manuscript that could make an important contribution to our understanding of the roles of micro RNAs at metamorphosis, and potentially of how sex-specific differences arise during metamorphosis. Strengths of the paper include the functional validation of microRNA binding, in vitro and in vivo-, as well as the characterization of sesquiterpenoid and ecdysteroid titers. The authors have also used CRISPR to generate specific knock-outs of miR-34 and miR-277. The transcriptomes will be a resource for future work to mine for differences in gene expression during metamorphosis.

      Weaknesses:

      (1) Spatial Expression of miR-34 and miR-277. If miR-34 and miR-277 regulate AstC-R1 and Kr-h1, then they must be expressed in the same cells. Although the authors show that the microRNAs do bind to the transcripts of AstC-R1 and Kr-h1 in S2 cells, and miR-277 binds AstC-R1 and Kr-h1 in vivo whole-animal homogenates, we do not know if the microRNAs are ever in the cells where AstC-R1 or Kr-h1 are expressed. AstC-R1 is only expressed in a few cells in the brain, so it is not at all certain that it is co-expressed with either microRNA. The creation of enhancer lines or in situ hybridization in Drosophila is straightforward and would sort this out.

      We thank the reviewer for the positive and constructive comments. We agree with the reviewer, and have pointed out the limitation in the main text as follows:

      Lines 307-309 - “Further exploration of the spatial expression between miR-277, miR-34, Kr-h1 and AstC could bring new insights to the regulatory effects they have during metamorphosis”.

      (2) Phenotypes. Although a double deletion was used and specific knock-outs of both miR-34 and miR-277 were generated, the analysis of the mutants is very superficial. For the homozygous deletion of both microRNAs miR-34 and miR-277, only a decrease in survivorship was observed a full six days after adult eclosion - after the end of metamorphosis. No phenotype for either miR-34KO or miR-277-KO was given. The authors cite the work of others who have found specific phenotypes after manipulation of sesquiterpenoids or ecdysteroids, like Riddiford and Ashburner, but do not use any of these many studies to help them characterize the phenotype. If the loss of miR-34 and miR-277 affects so many pathways (including MAPK signaling, TGF-beta signaling, FoxO signaling, and Wnt signaling), as well as global titers of metamorphic hormones, then there shouldn't there be something different in the development to discuss?

      We thank for reviewer for pointing out the phenotypes. We did not detect morphological or developmental abnormalities during larval-pupal transitions in the different knockout mutants, and have now included the previously established research on miR-34 in the main text, shown as follows:

      Lines 305-307 - “MiR-34 has been documented to maintain the neural proteostasis while suppressing age-associated decline (Liu et al. 2012; Kennerdell, Liu and Bonini 2018; Srinivasan, Tran and Bonini 2022).”

      (3) I think the reliance on GO term enrichment is getting in the way of biology. For instance, I would not describe Kr-h1 as a sesquiterpenoid biosynthesis pathway gene. Yet the authors say they were motivated to examine microRNA regulation of Kr-h1 because they saw differences in levels of the sesquiterpenoid biosynthesis pathway between WL3 and WPP, a period which also saw differences in expression of some microRNAs. I understand that Kr-h1 expression is regulated by JH, a sesquiterpenoid, but it is not directly involved with JH production, so relying on GO term enrichment has made the decision to focus on Kr-h1 feel arbitrary.

      We have stated out the rationale and rephrased that Kr-h1 as sesquiterpenoid responsive gene in relevant sentences in the Results, shown as follows:

      Lines 135-145 - “Utilising an established cell culture dual-luciferase reporter assay (Hui et al 2013); we have validated 7 microRNAs (miR-8, miR-277, miR-958, miR-1000, miR-1002, miR-2283, miR-2280) that can downregulate the expression of Drosophila genes involved in JH synthesis, responsive, or signalling genes to varying degrees, including allatostatin C (AstC), allatostatin C receptor 1 (AstC-R1), allatostatin C receptor 2 (AstC-R2), Krüppel homolog 1 (Kr-h1), Chd64, juvenile hormone binding protein (JHBP), juvenile hormone diol kinase (JHDK) and juvenile hormone esterase (JHE) (Figure 1C). Among these genes, Kr-h1 or Kruppel homolog 1, is a JH-dependent transcription factor (responsive gene), and have been found to be regulated by another microRNA in cockroaches (Lozano et al 2015); while AstC-R1 or allatostatin C receptor 1, is a G-protein coupled receptor for neuropeptide allatostatin C which regulates juvenile hormone synthesis (Stay and Tobe 2007).”

      (4) The transcriptomes of miR-34 and miR-277 should have revealed genes encoding members of the sesquiterpenoid biosynthesis pathway as well as AstC-R1 and Kr-h1, but neither was mentioned. The functional tests of miR-34 and miR-277 were performed because they were shown to affect the levels of expression of genes in the sesquiterpenoid biosynthesis pathway. Figure 2 shows a significant decrease in AstC-R1 and Kr-h1 transcripts after the loss of miR-34 and miR-277. However, the results do not mention either (Lines 250-264). Instead, there is a list of 10 different GO terms (like arginine and proline metabolism or fatty acid degradation) that were enriched in miR-34 and miR-277 transcriptomes. If any of those ten types have any relationship to Kr-h1, AstC-R1, or metamorphosis, that has not been explained.

      We have now explicitly stated that Kr-h1 is significantly downregulated in the miR-277-KO transcriptomes consistent with our qPCR validation, as well as the transcriptomic analyses were used to understand genome-wide changes, shown as follows:

      Lines 211-217 - “Among these differentially regulated genes, Kr-h1 was significantly downregulated in the miR-277-KO mutants (Supplementary Table S2), consistent with our qPCR validations (Figure 2B). On the other hand, AstC-R1 was not classified as differentially regulated based on the in silico analyses threshold of the whole-body RNA-seq data, and whether this was due to its restricted expression in a small subset of brain neurons would require further experiments to differentiate between different scenarios.”

      Lines 255-259 - “It implies that miR-277 could have conserved regulatory roles of sesquiterpenoid and related pathways, and also divergent roles between sexes during metamorphosis with signaling pathways associated with cell proliferation, wing development, nutritional support, and endocrine regulation (Pan et al 2018; Jiang and Edgar 2009; Gibbens et al 2011; Toyama et al 2014).”

      (5) Not enough care was taken in describing the stages. The methods describe wandering larvae (WL3) and white pre-pupa (WPP) for the transcriptomes, but in the text, different terms are used, like "larva", "pupa" and "L3 larvae instars" "early pupae" "late L3". Also, it seems like the small RNA libraries for sequencing were taken from "L3 larvae", but the stage of the L3 larvae was not mentioned. Staging is important, especially during metamorphosis, since differences in expression are expected to exist between different stages of L3, between early vs late wandering, and between WPP and early pupal stages.

      We thank the reviewer for highlighting the importance of staging clarity, and have now revised both the Results and Methods to ensure all stages are defined explicitly and used consistently throughout the manuscript. In our dataset, all references to “L3 larvae” or “larva” correspond specifically to wandering third-instar larvae (WL3) which that have exited the food, display 1/3 to 1/4 gut contents, and show protruding anterior spiracles. All references to “pupa”, “pre-pupa”, or “early pupae” refer to white prepupae (WPP), defined as immobile individuals with fully everted spiracles and no movement when gently probed.

      Recommendations:

      (1) Lines 171-173. The genotype of the deletion line is given, but what is the genotype of the flies that were used? Were the balancers and chromosomes with mutations included in the analysis? I cannot tell from the description.

      The miR-277-34-KO line was used and significant decrease in survivorship was observed in double knockout line. The information is now included as follows:

      Lines 158-164 - “In the homozygous loss of function of miR-277/34 cluster fly (w[*]; Df(3R)mir-277-34-KO, TI{w[+mW.hs]=TI}mir-277-34-KO/TM3, P{w[+mC]=GAL4-twi.G}2.3, P{UAS-2xEGFP}AH2.3, Sb[1] Ser[1]; Chen et al 2014), we found that the heterozygous knockout individuals displayed low to almost no lethality, while the survivorship for the double knockout flies fell significantly with 77% of the double knockout flies dying within five days after eclosion (Figure 2A).”

      (2) The introduction and discussion are too broad. I would expect some information about what is happening to ecdysone and juvenile hormone titers during metamorphosis. Additional detail should be given to the regulation of the corpora allatum, since the Ast-C1 gene and sesquiterpenoid levels were a major part of the results. I had hoped the discussion would help to synthesize the details within the results, particularly the differences between sexes. Instead, the discussion is very general, citing other papers that are related, but without tying the results within those papers to specific results in this paper.

      We thank the reviewer. The following information is now included in the main text:

      Lines 47-53 - “Multiple ecdysone titre peaks were observed during Drosophila development with each pulse corresponds to a developmental transition (Kannangara et al., 2023; Scanlan et al., 2023). For instance, there are one small and another big one triggering wandering in larvae and pupariation, respectively. For sesquiterpenoid juvenile hormone, its titre in corpora allatum changes in different developmental stages (Zhang et al., 2022) and generally decreases closer to the end of larval stages for pupariation (Dubrovsky 2005).”

      (3) I found the text to be unclear, and important details were missing. There are many different cases where titers or gene expression went up or down, in males or females, but it is difficult to understand if these were expected or not, or what to make of all the differences. I also do not have a clear idea of what the hypothesis is. I had thought that the microRNAs were promoting the degradation of AstC-R1 or Kr-h1, so their loss should result in higher levels of AstC-R1 or Kr-h1 transcripts. However, Figures 2B and C show that there are lower levels of AstC-R1 and Kr-h1 transcripts in the loss of either miR-34 or miR-277. How does this fit with what is known of the progression of metamorphosis? A model built from the results to this point could help to organize the text.

      The following information is now included in the main text, shown as follows:

      - Lines 172-177 - “We then compared the expression of Kr-h1 and AstC-R1 of miR-277-KO, miR-34-KO, and between KO mutants and wild type flies using quantitative real-time PCR. The expression level of Kr-h1 was found to be significantly downregulated in both sexes of WL3 and WPP miR-277-KO mutants, coherent to the RNA-seq results; whereas a reduction was only noted in the male WL3 for miR-34-KO mutants but not in RNA-seq (Figure 2B; Supplementary Table S2).”

      - Lines 178-186 - “On the other hand, expression level of AstC-R1 determined by real-time PCR was found to be upregulated during metamorphosis in wild-type (w1118) flies, but not for either miR-277-KO or miR-34-KO mutants (Figure 2C; Supplementary Figure 6). This is in agreement with the transcriptomic analyses where AstC-R1 was only up-regulated during male and female metamorphosis in the wild-type fly (Supplementary Table S2). In addition, we also found that the expression level of AstC-R1 in WPP of both miR-277-KO and mir-34-KO flies were significantly different to that of the w1118 flies (Figure 2C). Together, these data demonstrated that Kr-h1 and AstC-R are stage-specifically regulated by miR-277/34 in vivo.”

      Reviewer #2:

      Summary:

      This study proposes that the microRNA cluster miR-277/34 controls the generation of sexual dimorphism in Drosophila melanogaster during metamorphosis by acting on specific hormonal and developmental gene pathways.

      Strengths:

      Using a combination of mRNA and small RNA sequencing together with genome-wide in silico and in vitro analyses the authors identified a microRNA cluster that may be involved in metamorphosis and the generation of sexual dimorphism in Drosophila melanogaster.

      Weaknesses:

      Biological validation of the identified sexually dimorphic genes and a detailed understanding of how the microRNA cluster miR-277/34 might be involved in the regulation of sesquiterpenoids are needed.

      Major suggestions:

      (1) If AstC-R1 and Kr-h1 are targets of the miR-277/34 cluster and cause their downregulation, it is not clear why there would also be a decrease in the levels of these genes in the miR-277/34 mutants. This would suggest that the mechanism is not straightforward and that further epistatic experiments should be carried out in order to clarify this issue.

      We thank the reviewer for the positive and constructive comments. We agree that targets participate in endocrine feedback loops is dynamic, which could be the case for Kr-h1 and AstC-R1 during the larvae-pupae transition as they are components of the sesquiterpenoid hormone regulatory network. Epistatic analyses would be useful for future work and seem to be beyond the current scope of this study. The following information is now included in the main text:

      Lines 184-187 - “Together, these data demonstrated that Kr-h1 and AstC-R are stage-specifically regulated by miR-277/34 in vivo. Further epistatic experiments would be needed to understand the dynamics between and the hormone actions.”

      (2) The changes in the expression levels of AstC-R1 in pupae of miR-277-KO and mir-34-KO flies must be accompanied by photos of the respective larvae and pupae, as well as an analysis of the larvae-pupa transition on the mutants by gender.

      We thank the reviewer for this suggestion. We have now added reference images illustrating the WL3 and WPP stages for w1118 in the Appendix as suggested (Author response image 1). We have analysed the larvae-pupae transition in both sexes of miR-277-KO and miR-34-KO mutants, directly comparing them with stage-matched w1118 controls, and we did not observe any detectable differences in larval morphology, pupal morphology, anterior spiracle eversion, gut-purge status, or timing of pupariation between the mutants and controls.

      Author response image 1.

      (3) Biological validation of the identified sexually dimorphic genes in vivo will be necessary for the support of this work.

      We have now included additional quantitative RT-PCR validation of key insect hormone pathway genes in male and female WL3 and WPP stages of wild-type, miR-277-KO, and miR-34-KO flies (Figure 5). These additional analyses confirm sex-specific expression differences in both wild-type and mutant backgrounds, supporting dimorphic endocrine regulation. The main text are also revised and shown as follows:

      Lines 276-287 - “To understand how different hormones such as juvenile hormone (Belgacem and Martin, 2002) involved during metamorphosis in both sexes, we selected and tested a selection of candidates from both sesquiterpenoid and ecdysteroid hormone pathways, and found JHAMT, Spok, CG40486, and CG10962 showed differential expression during transition from WL3 to WPP in mutant and/or wild-type fly lines in the transcriptome analyses (Supplementary Figure 16). To further examine these RNA-seq patterns, we performed RT-PCR on nine candidate genes from WL3 to WPP in WT and mutant fly lines. In the larval-pupal transition comparison, CG40486 was consistent down-regulated in WT males and females in both RNA-seq and RT-qPCR datasets. In the mutant-vs-WT comparison, CG9360 showed concordant downregulation in miR-277-KO WPP females. Other tested genes showed more condition-specific patterns matching between the transcriptomic and RT-qPCR analyses. Further validation and mechanistic studies will be required to define how these hormone-pathway changes contribute to the larval–pupal transition.”

      Reviewer #3 (Public Review):

      Summary:

      The authors show convincingly the complexity of gene up- and down-regulation at the outset of metamorphosis and identify substantial differences between the two sexes, even at this early time in development. The complexity of microRNA expression and the difference between the sexes are also nicely laid out. The functional significance of these differences, though, is harder to establish. The authors have focused on the roles of two families of developmental hormones, the ecdysteroids, and the juvenile hormones. The emergence of sex-specific differentiation of organs during metamorphosis is clearly downstream of the action of ecdysteroids and/or JH, but there is no evidence that the presence or lack of these hormones has any effect on the sexual identity of organ systems - i.e., that manipulations of JH or ecdysteroid result in either the masculinization or feminization of individuals or their organs. The precedence for the linkage of these hormones to sex determination is the 2002, Belgacem & Martin study, which describes the effects of JH on fly locomotion. These authors show that the number of stop/start bouts is sexually dimorphic, and removal of JH in males shifts their frequency into the female range while giving treated males exogenous JH moves it back. While this is referred to as a "feminization" of male behavior, this quantitative shift in frequency is not as compelling as would be a qualitative shift -- for example, the removal of JH causing males to show egg-laying behavior (a result that has never been seen). Also, these effects are in a fully mature system, rather than at the early metamorphic time examined in the present paper. In driving and coordination metamorphosis, JH and ecdysteroids are intimately involved in sexual differentiation, but I know of no compelling evidence that they play a role in sex determination.

      We thank the reviewer for the positive and constructive comments. We agree that our findings advanced on the sexual differences in gene regulation during metamorphosis rather than establishing direct evidence of juvenile hormone (JH) or ecdysteroids in influencing insect sex determination. In addition to rephrasing at various parts of the main text (see below), we have also carried out additional experiments in obtaining and analysing transcriptomes of late larvae and early pupae of both sexes in the wild-type of another species (Drosophila virilis), which showed similar KEGG pathways enriched. The revised parts are shown as follows:

      Lines 289-303 – Results section

      “Metamorphosis between different sexes in D. melanogaster and D. virilis

      In D. melanogaster, the transition from WL3 to WPP shows increased carbohydrate/energy metabolism and ER protein processing, with decreased lipid metabolism, detoxification (cytochrome P450), and redox/cofactor pathways (folate, glutathione, retinol); while PM-LM exhibits broader negative enrichment in translational and RNA-processing machinery (ribosome, spliceosome, proteasome, RNA degradation, ubiquitin-mediated proteolysis) and in signaling modules (mTOR, MAPK, FoxO, Hippo), whereas PF-LF shifts are smaller and less coordinated by GSEA (Supplementary Figure S15). On the other hand in D. virilis, core features match that of D. melanogaster from WL3 to WPP, carbohydrate/energy metabolism and ER protein processing increase, while lipid, detoxification, and redox/cofactor pathways decrease. The sex bias is inverted relative to D. melanogaster as PM-LM shows limited up-regulation, whereas PF-LF displays broader, coordinated repression involving amino-acid metabolism (glycine/serine/threonine; cysteine/methionine), lysosome, ABC transporters, and longevity-related signaling, yielding the largest DEG set in females (Supplementary Figure S15). These data strongly suggested the sexual differences in gene regulation during metamorphosis in insects is not species-specific.”

      Lines 354-361 – Methods section

      “D. melanogaster w1118 and D. virilis were reared at 25 C, 45-50% relative humidity and 14:10 hour light-dark cycle. Sexes of wandering L3 larvae instar (WL3) were differentiated under the light dissecting microscope by the presence (male) or absence (female) of gonads (Supplementary Figure 1). Collections were restricted to larvae wandering out of food and ready for pupariation. WPP were defined as immobile puparia with everted spiracles and a uniform white cuticle that were no longer moving even with external stimuli. A total of three biological replicates (Whole-body of 1 individual was served as per biological replicate) per stage and sex were obtained for D. melanogaster and D. virilis.”

      While the summary of the effects or removal of specific microRNAs on the components of the biosynthetic pathway for the JHs and ecdysteroids (Figure 2E ,F) is quite compelling, I am concerned about the effects of the removal of mir-277 and mir-34 on the levels of both the JHs and 20E. My concern centers around the data from the control group (w[1118] animals in Figure 2D). These data are the first report of a marked sex difference in the titer of either JH or ecdysteroid at the start of metamorphosis in Drosophila. As expected, males show a 10-20 increase in levels of JH III, JHB3, and 20E between the L3 stage and the white puparium, but, surprisingly, the levels of these hormones in female L3 larvae are equal to or greater than that seen at pupariation! These data for females run counter to over 50 years of work on the effects of ecdysteroids in Drosophila!

      We thank the reviewer and agree that the endocrine profiles of WL3 females require careful interpretation, given the late-L3 to WPP transition represents a highly dynamic window in which ecdysteroid and sesquiterpenoid titres can fluctuate over short developmental intervals. The revised text is shown as follows:

      Lines 322-326 – “Given the WL3 to WPP transition represents a highly dynamic window in which ecdysteroid and sesquiterpenoid titres can fluctuate over short developmental intervals, future experiments on narrower early-/mid-/late-wandering timepoints will be required to understand the dynamics of the microRNAs and hormones.”

      As far as I can gather from the paper, the L3 data were obtained using wandering larvae. This stage lasts for about 12 hours and ends with pupariation. Larvae from this period need to be used with caution for hormone studies. Levels of both JH and ecdysteroid are low as larvae leave the food but rapidly rise to their peak levels at the white puparium stage 12 hours later. To deal with the rapidly changing hormonal landscape through this period, the researchers have used physiological markers to track this progression. Initially, it was the sage of "puffing" of the giant salivary gland chromosomes, but, for bulk collection of staged larvae, larvae are fed on food containing a blue dye, and progression is tracked by the loss of blue coloring from the gut. I could not find if the authors had any criteria for selecting larvae during the wandering period. Male and female larvae grow to different sizes. Might this difference in growth be biased when larvae were selected during their wandering phase?

      We thank the reviewer and agree that WL3 represents a hormonally dynamic 12-hour window. To minimise developmental heterogeneity, WL3 larvae were selected for larvae that had completely exited the food and climbing upwards of the fly vials. To ensure tight developmental synchrony, pupating individuals from the same batch were collected 4 hours later, allowing us to match WL3 and WPP animals from a single, developmentally coordinated cohort. This paired-batch collection tried to minimise variation arising from differences in timing of wandering onset. As for the differences between the body sizes of male and female larvae, we minimise size-dependent bias, hormone titres measurement were measured to ng hormone per gram of tissue in our study, which corrects for sex-specific differences in mass. The information is now included in the main text as follows:

      Lines 276-287 – “To understand how different hormones such as juvenile hormone (Belgacem and Martin, 2002) involved during metamorphosis in both sexes, we selected and tested a selection of candidates from both sesquiterpenoid and ecdysteroid hormone pathways, and found JHAMT, Spok, CG40486, and CG10962 showed differential expression during transition from WL3 to WPP in mutant and/or wild-type fly lines in the transcriptome analyses (Supplementary Figure 16). To further examine these RNA-seq patterns, we performed RT-PCR on nine candidate genes from WL3 to WPP in WT and mutant fly lines. In the larval-pupal transition comparison, CG40486 was consistent down-regulated in WT males and females in both RNA-seq and RT-qPCR datasets. In the mutant-vs-WT comparison, CG9360 showed concordant downregulation in miR-277-KO WPP females. Other tested genes showed more condition-specific patterns matching between the transcriptomic and RT-qPCR analyses. Further validation and mechanistic studies will be required to define how these hormone-pathway changes contribute to the larval–pupal transition”.

      The other hormone-related issue is the expression of Kr-h1 during larval stages and metamorphosis (Figure 1G). Kr-h1 is the main target of JH and Kr-h1 expression is often used as a proxy for the JH titer. The authors report that peak Kr-h1 expression occurs in the L3 (when the JH titer should be lowest!) and that it drops at wandering. This pattern is counter to that reported in the literature (e.g., FlyBase, ModEncode).

      We have now revised the main text as follows:

      Lines 204-207 – “It should be noticed that the analyses of this study was based on RNA taken from whole-body rather than individual tissue, and further experiments will be required to dissect the dynamics of Kr-h1 and different hormones both spatially and temporally.”

      Besides dealing with the issues above, it would be useful in the background to establish the difference between "sex determination" and "sexual differentiation". I was surprised that the authors could deal with this subject without ever mentioning the sex determination pathway - e.g., double sex, transformer, etc.

      In this study, one major focus was on the differences between sex during metamorphosis rather than the sex determination pathway. We have included the reviewer’s suggestion by adding the following information in the main text, shown as follows:

      Lines 73-75 - “In insects, various genes related to sex determination or showing sexual dimorphic features have been revealed, including doublesex and transformer (Shukla and Nagaraju, 2010; Deng et al, 2025).”

      Line 61: at the time that the authors are considering, -- the transition from larva to pupa - the main controller of ecdysone production in the prothoracicotropic hormone, not ecdysis triggering hormone.

      Thanks for the reviewer’s suggestions. The main text is now written as follows:

      Lines 44-47 - “In general, these three hormones work in a cooperative manner, where ecdysteroid 20-hydroxyecdysone controls the timing of ecdysis or molting, prothoracicotropic hormone and the insulin-like peptides act as regulators of ecdysteroid synthesis, and sesquiterpenoid hormones such as juvenile hormone (JH) regulates metamorphosis based on their titre levels.”

      Line 71: it would be useful to name the main microRNAs that have been implicated in regulating insect metamorphosis.

      The main text is now written as follows:

      Lines 60-63 - “Previous studies have identified a number of microRNAs contributing to the regulation of insect metamorphosis such as let-7, miR-125, and miR-2 family (e.g. Caygill and Johnston 2008; Sokol et al 2008; Lozano et al 2015).”

      For Figures 2E and F as well as 3C (and a number of supplementary figures), it would be useful to provide some quantitative representation of the level of activation or suppression (as done in Figure 3B) rather than just Red or Green

      The trend of expression change of genes in selected pathways and detailed quantitative information can now be found in the supplementary file.

    1. eLife Assessment

      This important study used several genome-wide approaches to compare dormancy in S. pombe spores and killifish diapause embryos. The findings include a trade-off between longevity and heat resistance in yeast spores and, in both these systems, a relatively higher expression of ribosomal-protein mRNAs but not proteins. While the evidence for the findings is overall solid, some interpretations should still be tempered. The large-scale datasets provide an excellent resource for future research in the dormancy field.

      [Editors' note: this paper was reviewed by Review Commons.]

    2. Reviewer #1 (Public review):

      Summary:

      In the manuscript entitled "Gene function and expression profiling in yeast spores, killifish diapause embryos, and their post-dormant offspring cells" the authors analyze dormancy at three levels: functional, transcriptomic and proteomic, using Schizosaccharomyces pombe spores, diapausing killifish embryos and human dormant cancer cells as models. The authors performed a genome-wide Bar-seq screen for gene deletions in fission yeast spores and then compared changes in mRNA and protein expression in young and old and heat-stressed cells in yeast and killifish. The main conclusions of the work include that dormancy is a dynamic not static state. The authors conclude that specific biological processes, rather than particular genes, are conserved across organisms during the dormancy, and that there is a clear disconnect between the mRNA and protein kinetics and behavior, particularly for ribosomal proteins and processes related to autophagy and mitochondrial metabolism.

      In general, the manuscript is well structured and written, with carefully designed figures. The authors are also generally aware of the limitations of the work. The manuscript is interesting and valuable, but some interpretations should be moderated.

      Major comments:

      (1) The data presented provide in general good support for majority of conclusions presented in the manuscript. However, less support is provided for the theories that: there is a universal "core expression signature" of dormancy; the mRNA/ribosome protein uncoupling reflects a specific mechanism of preparation for exiting dormancy; and the observed changes provide evidence for an epigenetic "memory" of dormancy. These elements are currently more interpretive than evidentiary. I would recommend softening the language in these places and clearly labeling them as hypothetical.

      (2) The authors propose that spores and post-dormancy cells retain a memory of time spent in dormancy or stress exposure. However, sporulation is also generally expected to reset or remove accumulated damage. Please clarify whether the observed long-term effects reflect classical "memory" of the cell, persistent regulatory state, or selection of cells with different fitness (carry-over effects). I would be careful in interpreting the effects as a form of memory associated mostly with epigenetic control - the statement seems stronger than what the data directly demonstrate.

      (3) Validation of a small set of top hits by independent assays would substantially strengthen the conclusions. For example, targeted qPCR and/or biochemical assays for key genes (like ribosomal and autophagy) or pathways would help confirm the most important transcriptomic and proteomic trends. Without this, the suggestion of, for example, translational regulation remains possible but unproven.

      (4) Overall, the study appears reproducible, with detailed methods, biological replicates, and appropriate statistical analyses. However, reproducibility could be improved by more clearly describing the limitations of the comparisons (especially with the published datasets) and the data-processing parameters, particularly for correlation and functional enrichment analyses. Some comparative conclusions are based on data from different studies, under different conditions and often with different depths of coverage. It is not always clear to what extent interspecies comparisons are fully methodologically similar.

      Significance:

      This is an ambitious work that combines a genetic screen with multi-omics analyses and cross-species comparisons. The novelty is less in known roles of autophagy, mitochondria and translation, but rather in integrating them into a comparative framework across S. pombe spores, killifish diapause embryos, and dormant human cancer cells, combined with Bar-seq screening and multi-omics. Its greatest strength is its coherent, broad concept: dormant cells are not passive, and the regulated processes and functions during dormancy are evolutionarily conserved rather than the individual set of genes. The contribution of this work lies more in demonstrating process-level conservation than in identifying a single universal gene. This significantly expands the literature on dormancy, but rather in terms of a framework of biological organization rather than a single mechanism.

      The weakness of the manuscript is the tendency to draw broad, sometimes overly strong mechanistic conclusions based primarily on correlational data (sometimes with poor statistical significance) and on relative, rather than absolute, expression. The lack of biochemical validation, in my opinion, lowers the impact of the paper.

      Audience: This work will be of interest especially to aging and dormancy specialists and comparative biologists. It is important to note that this is not a purely technical or model-specific work, but it has the potential to impact researchers interested in homeostasis, translation and adaptation to stress.

      My expertise: My expertise is mostly in aging, sporulation, cell cycle control and ribosome biogenesis.

    3. Reviewer #2 (Public review):

      Summary:

      In this manuscript, Hassan et al. examine dormancy in fission yeast spores and killifish embryos in diapause. The authors first performed a genetic screen in yeast spores to identify genetic modulators of spore longevity and resilience to heat stress, finding that there is a trade-off between stress resilience and longevity. Next, the authors performed transcriptomic and proteomic profiling of yeast spores and killifish diapause embryos, comparing active and dormant states, early and late dormancy, and with and without heat stress in each model. Comparison between the two models identified a shared set of gene expression changes in dormancy. Lastly, the authors tested whether stress in dormancy is able to reshape the transcriptome or proteome following exit from dormancy, finding that heat stress altered gene expression post-dormancy in both yeast spores and killifish diapause embryos.

      This study is interesting because it provides valuable transcriptomic and proteomic datasets across a large number of conditions in both yeast spores and killifish embryos. Furthermore, these datasets reveal several interesting insights about dormancy, including shared gene expression changes in dormancy across diverse organisms, a trade-off between stress resilience and longevity in yeast spores, and memory of heat stress following exit from dormancy.

      This interesting manuscript would be strengthened by additional characterization of the functional consequences of these gene expression changes.

      Major comments:

      (1) The finding that a set of gene expression changes in dormancy is shared between yeast spores and killifish diapause embryos is very interesting and raises the question of whether the genetic determinants of stress resilience or longevity in dormant states are also shared. For instance, do any genes identified in the genetic screen as critical for resilience to heat stress in yeast spores also show a similar phenotype in killifish diapause embryos?

      (2) The authors show that gene expression changes in dormancy (old vs. young spores and late vs. early diapause) are poorly correlated with aging in stationary-phase yeast cells (Figure 4E) and killifish brain (Figure 5E). The authors suggest that this poor correlation might reflect a lack of aging during dormancy. Interestingly, the authors also show that in both yeast and killifish models, both time and stress in dormancy result in gene expression changes following exit from dormancy. Are these post-dormancy differences correlated with aging in stationary-phase yeast cells and adult killifish? Additionally, could the authors compare these changes with age-related changes in different adult killifish organs?

      (3) In Figures 1C, 4F, and 5F, the authors find significant correlations between two comparisons with a shared condition (e.g. 2-week unstressed spores in Figure 4F). Using the same samples in both comparisons can inflate the correlation between the two sets of fold-changes because the two comparisons are not made independently. The authors should ensure that the correlations found in these figures remain significant even when different samples are used for the shared condition in the two comparisons.

      Significance:

      This study is interesting because it provides a comparison between different states of dormancy and identify shared pathways. This paper should appeal to a broad audience, including aging, stress pathways, and quiescence/dormancy.

    4. Reviewer #3 (Public review):

      Summary:

      In this study, the authors use several genome-wide approaches to find commonalities and differences between distinct dormancy models, including S. pombe spores, killifish diapause embryos, and human dormant cancer cells. As G0 states are ubiquitous in nature, yet still understudied, this study provides not only a clear demonstration of an evolutionarily-conserved component in G0 regulation, but also a rich resource of genomics datasets, that will be especially of interest to S. pombe and killifish researchers.

      The main conclusions of the study are that: (1) there are commonalities in dormancy programs across eukaryotic evolution; (2) the uncoupling between transcriptome and translatome is more pronounced in non-dividing cells, particularly at ribosomal protein coding genes; and (3) dormant cells retain dynamic responses to stress, changing their cellular state and maintaining this difference.

      Overall, the study is clearly written, provides large data resources for future research in the dormancy field, and the conclusions are supported by the presented evidence (although point 3 would require further experimental clarification, see below).

      Major comments:

      A key consideration when comparing transcriptomes of cycling and dormant cells is the overall RNA quantity in the cells; however, it is not made clear in the manuscript whether this overall level was compared and/or if RNA-seq used spike-in quantification. However, this will directly affect some conclusions; for example, there is a conceptual difference between a transcript upregulated in spores and a transcript that appears upregulated in spores but is in fact 'less repressed than average' (resulting in higher FC). While the overall transcript diversity as described will be not affected, this analysis can provide important context. For the highlighted example of ribosomal protein mRNAs, this scenario could be what resulted in an apparent mRNA increase but protein decrease (which would be interpreted as both mRNA stabilization and translation inhibition); if the total mRNA is much lower, then the mRNA levels may be similar (which would then be interpreted as no change in transcription nor mRNA stability, but highlight the importance of translation inhibition).

      This point was more clearly described in a previous seminal study by the authors (Marguerat et al 2012), and so should be made clearer here as well. What is the overall RNA level in cycling cells compared to quiescent cells, and compared to spores? Likewise, what is the overall reduction in total protein levels? Was the overall reduction comparable to the previous study? These overall levels should at least be quantified, which is a straight-forward experiment. The current mention of this limitation (two sentences in the last paragraph of the conclusion) is not enough.

      Accordingly, the term "induced" (in Fig 2 for example) may be misleading, and could be separated into truly "induced" genes and "relatively induced" genes, when normalized to absolute levels. Likewise, Fig S2A and S2I are misleading due to this effect (normalization will tend to show a similar number of up-regulated and down-regulated genes). These panels should be clarified by plotting next to the relative quantification (current panels) a comparison taking overall RNA level into account. This will very likely strongly increase the number of downregulated genes and decrease the number of upregulated genes, and the new lists can also be subjected to GO analysis.

      The same issue would be important to discuss for the killifish experiment as well.

      For spore offspring stress experiments, the estimated number of cell divisions after spore germination should be indicated. It appears over 20, which would confirm the authors' conclusion that an epigenetic state is induced and persists over multiple rounds of replication and underlie improved heat resistance, indeed hinting at transcriptional memory through chromatin modification. As the phenotype is tested at a population-level, it is unclear whether every spore is equally able to impart this phenotype, and for how long the stress resistance response persists. Moreover, it may be clearer to test heat resistance in log-phase cells, as in the current experimental setup cells are grown to stationary-phase before plating; however, stationary-phase cultures are more heterogenous and more heat-resistant.

      Note also that using 3-fold serial dilutions (instead of more commonly used 10-fold) means that the differences shown in Fig 3M are mild. Plating duplicates (at log-phase) and imaging at several times of growth would be necessary to have a clearer view of the result.

      Significance:

      Overall, the study provides a proof-of-concept that dormancy programs across vastly different eukaryotes display commonalities. This is not an entirely new concept, but this is the first demonstration of it using S. pombe and turquoise killifish and will therefore be of interest to a wide audience of researchers interested in quiescence and dormancy in general. A key finding (albeit also not entirely new) is the transcriptome/proteome difference, which appears increased in dormant cells; this hints at the existence of key post-transcriptional regulatory mechanisms in dormant and G0 cells.

      A current key limitation is that only relative quantifications are discussed, which can currently give a misleading idea of overall transcriptional activity in dormant cells. Another limitation is that differentially-regulated genes, proteins and GO processes are mentioned, but with relatively limited biological interpretation, making some sections of the manuscript lengthy and descriptive.

      Advance: The authors provide a vast amount of datasets for fission yeast spores (transcriptome, proteome, differential viability of a mutant collection) and killifish, which will be a very useful resource for researchers in the field. The authors identified several genes and processes that are commonly differentially regulated in dormant cells across several systems, including autophagy and ribosomal protein genes, which were previously known to be required for G0.

    5. Reviewer #4 (Public review):

      Summary:

      This manuscript from Hassan et al presents a comparative study of the hallmarks of dormant cells in different systems. The authors first use a bar-coded knockout set to identify genes important for persistence of fission yeast spores in the dormant state (up to 6 months) and then use RNA seq and Mass spec to examine changes in transcript and protein abundance as fission yeast spores enter and exit dormancy. They then perform a similar transcriptomic and proteomic profile of Killifish embryos in diapause. The authors note certain functional classes of genes (e.g. autophagy-related genes or ribosomal proteins) that are similarly upregulated during dormancy in both systems and show that this applies in human dormant cancer cells as well. Importantly, this pattern applies to genes broadly in these functional classes, but not necessarily to any particular gene within the class. Finally, the authors examine the effects of stress (heat shock) on the transcriptomes and proteomes of young and aged spores as well as mitotic populations germinated from those spores.

      Dormancy is relatively understudied at the cellular level and this study provides a number of potentially interesting observations, in particular the authors identify common features of dormancy-related expression changes in the different systems that may be a general hallmark of dormancy. Included in this is a fascinating disconnect between transcript and protein levels of ribosomal genes. That said, the work seems to be almost three different stories, none of which is entirely complete, and which only loosely fit together: (1) the identification of dormancy mutants in fission yeast; (2) comparative 'omics of dormancy in different systems; and (3) changes induced by stress in dormant cells.

      Specific comments:

      There is a dissonance between the apparent upregulation of autophagy genes during dormancy in both systems and the observation that loss of autophagy genes enhance dormancy. While the authors brush this off by noting "that genes regulated in a given cellular state often do not overlap with those functionally required for that state" (an argument that undermines their whole analysis), it raises the possibility that the process signatures they report are not, in fact, functionally linked to dormancy and could be coincidental. What would make the expression analysis more convincing would-be functional data showing that at least one of the upregulated genes in one of the systems is important for dormancy.

      It is not clear how the bar-coding experiment was controlled for mutants that make defective spores (e.g. poor spore walls) rather than being required for dormancy per se. As the authors note, several of the mutants with the strongest effects (shortening lifespan of the spores) have been previously identified in screens for sporulation or spore wall defects, consistent with those genes playing a specific role in proper spore assembly rather than a general function in dormancy.

      It would be helpful to make the data - or at least the lists of genes identified in the screen and the up and downregulated transcripts/proteins available as supplemental information.

      There are at least two different points of diapause in Killifish. It was not entirely clear which diapause was analyzed here. This should be clarified.

      The data that offspring from older or heat-shocked spores are more stress resistant (Figure 4M) is not convincing. Serial dilution assays as shown are more qualitative than quantitative and an actual quantitative measure (e.g. c.f.u.'s per cell plated) would be better and would allow statistical confidence to be calculated.

      Significance:

      This work provides a useful overview and comparison of gene expression changes occurring during dormancy in different systems. As this is a relatively understudied area the results are likely to be of great interest to researchers in this area. The primary weakness, as is often the case for systems studies, is that it provides a 'high level' view of changes, but it is unclear whether any of the specific changes documented are functionally relevant to dormancy.

      I am a cell biologist and not qualified to critically assess the quality/appropriateness of the different statistical measures used to analyze their expression data.

    6. Author response:

      General Statements

      We thank the four reviewers for their careful reading of the manuscript and for their constructive and insightful comments. Below, we outline the revisions we have already started or plan to make shortly in response to the reviewers' specific comments.

      Description of the planned revisions

      Reviewer #1:

      In general, the manuscript is well structured and written, with carefully designed figures. The authors are also generally aware of the limitations of the work. The manuscript is interesting and valuable, but some interpretations should be moderated.

      Major Comments:

      (1) The data presented provide in general good support for majority of conclusions presented in the manuscript. However, less support is provided for the theories that: there is a universal "core expression signature" of dormancy; the mRNA/ribosome protein uncoupling reflects a specific mechanism of preparation for exiting dormancy; and the observed changes provide evidence for an epigenetic "memory" of dormancy. These elements are currently more interpretive than evidentiary. I would recommend softening the language in these places and clearly labeling them as hypothetical.

      We agree, of course, that the core expression signatures determined for yeast, killifish and human may not extend to all other dormant states (and we do not call it universal in the manuscript). Also, the mechanistic nature of the ‘memory’ of dormancy is not known, and we have merely speculated on the possibility of an epigenetic memory for discussion, based on the known causes of similar phenomena. Similarly, we discussed plausible processes underlying the mRNA/ribosome protein uncoupling. We will make it clearer in the revised manuscript that these are only hypotheses at this point.  

      (2) The authors propose that spores and post-dormancy cells retain a memory of time spent in dormancy or stress exposure. However, sporulation is also generally expected to reset or remove accumulated damage. Please clarify whether the observed long-term effects reflect classical "memory" of the cell, persistent regulatory state, or selection of cells with different fitness (carry-over effects). I would be careful in interpreting the effects as a form of memory associated mostly with epigenetic control - the statement seems stronger than what the data directly demonstrate

      Our experiments focus on spores (post-sporulation) and their offspring cells, so these findings have no bearing on any reset that may occur during earlier sporulation. It is extremely unlikely that the observed ‘memory’ effects reflect the selection of cells with different fitness, because we started with genetically identical cells, findings were repeated independent biological replicates, and our conditions (stress and prolonged time) maintained the viability of most cells. However, we certainly agree that our results do not establish a mechanism for the observed post-dormancy ‘memory’ effects. Again, we merely discussed the plausible possibility of an epigenetic memory and will provide more nuanced interpretations in the revised manuscript.

      (3) Validation of a small set of top hits by independent assays would substantially strengthen the conclusions. For example, targeted qPCR and/or biochemical assays for key genes (like ribosomal and autophagy) or pathways would help confirm the most important transcriptomic and proteomic trends. Without this, the suggestion of, for example, translational regulation remains possible but unproven

      For transcriptome and proteome studies using current, reliable methods, transcripts or proteins from specific genes are not typically validated, especially when studies focus on global patterns and functional enrichments rather than on any specific genes. General induction of autophagy-related genes at both the RNA and protein levels is unlikely to occur by chance, and autophagy genes have previously been shown to be induced under similar conditions, including in the transcriptomes of killifish diapause embryos (see Fig. S2H). However, we agree that validation of the unexpected regulation of ribosomal genes in diapause embryos would strengthen this conclusion. In spores, the effect is weaker and depends on the control sample used. We have begun qPCR and Western validation of ribosomal protein gene expression in killifish embryos to independently confirm the antagonistic changes in relative expression at the RNA and protein levels. We will incorporate these results in the revised manuscript.

      (4) Overall, the study appears reproducible, with detailed methods, biological replicates, and appropriate statistical analyses. However, reproducibility could be improved by more clearly describing the limitations of the comparisons (especially with the published datasets) and the data-processing parameters, particularly for correlation and functional enrichment analyses. Some comparative conclusions are based on data from different studies, under different conditions and often with different depths of coverage. It is not always clear to what extent interspecies comparisons are fully methodologically similar

      We used two strategies to minimise the limitations of comparing our RNA-seq data with a published RNA-seq dataset (human dormant cancer cells): first, we considered only genes that were detected in all datasets being compared, so that any differences in sequencing depth would not bias the comparison; second, we used relative expression values normalized to their respective controls instead of raw values, using identical cutoffs for all studies (fold-change >1.5x; FDR <0.05), to make the cross-study comparisons more consistent. We will further clarify this and its limitations in the Methods and Results. Notably, we observed significant functional enrichments shared across all three model systems, something one would not expect if the comparison were strongly affected by differences in the analyses. A strength of our study is that the RNA-seq datasets used to compare killifish and yeast were both generated in our laboratory under standardised conditions and processed through the same bioinformatics pipeline, thus enhancing comparability. 

      Minor Comments:

      (1) Because the Bar-seq assay measures barcode abundance only after germination and regrowth, the phenotype may reflect not only spore survival but also germination efficiency and subsequent growth. Please discuss how the authors controlled for selection during regrowth and whether new mutations, clonal selection, or differences in ploidy could contribute to the observed effects.

      We highlight in the manuscript that “Bar-seq quantification of deletion mutants was carried out after spore germination and regrowth. Therefore, the abundance of each barcode is a product of the combined influence of spore survival, germination efficiency and growth, which may affect stress- or lifespan-associated phenotypes for some mutants, a compromise necessary to prevent barcodes of dead spores from being sequenced.” To partially correct for this effect, we used conservative cutoffs and normalised all barcode abundances to the 2-week reference timepoint, so mutants that were already depleted early on due to poor germination or growth should not confound the longevity analysis. Similarly, the heat-shocked samples were compared to unstressed control samples, and any differences between the two samples should therefore reflect the effect of heat stress. All timepoints and pools were grown under consistent, uniform outgrowth conditions. We used two independent biological replicates and pooled samples, so any effects arising from extremely rare events, such as new mutations or diploidization, should be negligible. Moreover, our genome-wide screens focus on global functional enrichments rather than any specific mutants. We will describe this strategy in more detail in the Methods section.

      (2) Please clarify whether spores were analyzed as pooled spores or after tetrad dissection, and how the authors ensured that colonies originated from single haploid spores rather than mixed events. Since ploidy can affect growth and lifespan, please indicate whether ploidy was checked in the recovered colonies or whether the experimental design excludes diploid formation during germination/regrowth.

      As indicated in the scheme in Fig. 1A, meiosis was triggered by crossing a haploid h+ with a haploid h- colony to generate diploid cells that immediately sporulate, using a colonypicking robot. All cells that do not form haploid spores after this step are then killed by heat. This is the same principle as applied for genetic interaction screens using synthetic genetic arrays (SGA). Thus, the haploid spores in each colony resulted from a cross between cells of different mating types carrying an identical deletion mutant. For both the Bar-seq screen and expressionbased spore analyses, spores were analysed as pooled populations without tetrad dissection. Our results refer to the population rather than the single-cell level. Unlike budding yeast, fission yeast cells do not normally grow as diploids and will not mate in the medium used for germination and regrowth. Naturally occurring, vegetatively growing diploid cells in fission yeast are rare and unstable; they would be at an equal growth disadvantage during regrowth from all conditions tested, thus minimising the possibility of a systematic bias caused by diploidy. Accordingly, we did not directly check for ploidy. We will describe this experimental design in greater detail in the Methods.  

      (3) The cross-species GO enrichment analyses may be affected by differences in annotation quality and gene coverage, especially between yeast and killifish. Please clarify how gene duplication, paralogs and annotation redundancy were handled and whether the conclusions remain robust after accounting for GO term similarity or annotation bias.

      Response:  The handling of orthology, paralogs, and annotation differences between species is described in the Methods. Importantly, yeast and killifish genes, and their GO terms, were not directly compared but via their corresponding human orthologs and GO associations, as these mappings are most reliable and comprehensive. Orthology relationships between fission yeast and humans were obtained from a manually curated list in PomBase (Wood et al. 2019), compiled from various sources. In some cases, the consensus ortholog from the major ortholog predictors (Compara, Inparanoid, OrthoMCL) is used. Distant orthologs have also been identified by PSI-BLAST matches. Other ortholog predictions come from experimental data demonstrating functional correspondence or involving membership of corresponding complexes. These predictions are aligned and submitted to Pfam before inclusion. PomBase’s approach ensures that the breadth of coverage exceeds that of any individual prediction method and includes many ortholog calls that are not detected by any automated method. Killifish-to-human orthology was inferred from BLASTp-based mapping to the human UniProt reference proteome, with additional hidden orthologs identified using the spotted gar proteome (Kelmer Sacramento et al. 2020). Spotted gar is a fish whose lineage diverged from teleosts before their genome duplication, and it was therefore used as a bridge to identify hidden orthologs between teleosts and mammals. All paralogs were included to ensure comprehensive coverage, although some may have specialised roles. 

      Thus, all genes were mapped to their human orthologs and their GO associations, and genes commonly regulated were identified by filtering for significantly induced or repressed genes across all models, as indicated. This procedure circumvents the bias in GO term annotation between yeast and killifish. Naturally, given the relatively poor annotation of the killifish genome, such comparative analyses may miss some orthologs. Nevertheless, we observed significant functional enrichments shared across all three model systems, something one would not expect if the comparison were strongly affected by differences in annotation quality or gene coverage. We will highlight this information more clearly in the revised manuscript. 

      (4) Please discuss whether the age of the mother cells could influence spore quality, lifespan, or stress resilience. Did the authors control for mother-cell age? I would like to know about a possible impact on spore quality and subsequent lifespan.

      Spores were always produced from freshly grown cells derived from frozen stocks, thereby controlling for any effects of cell age on spore quality and lifespan. Moreover, unlike budding yeast, fission yeast cells grow by symmetrical division, producing two daughter cells of equal size, so the concepts of mother cells and replicative ageing do not apply.  

      (5) It may be useful to briefly note how dormancy in plants differs from the fungal and animal systems studied here or to clarify that the present conclusions are limited to the models analyzed in this manuscript.

      We agree that this is an interesting topic, e.g. plant seeds. However, given that dormancy is an understudied cell state, there is not enough solid information to make meaningful comparisons at this point. Moreover, any comparative review of what is known in plants is beyond the scope of our study. In the revised manuscript, we will clarify that our findings are naturally limited to the fungal and animal cells analysed in our study.  

      (6) Please briefly justify the choice of heat shock as the acute stress condition. It would also be helpful to comment on whether oxidative stress (for example, by incubation with H2O2) might reveal similar or distinct aspects of dormant-cell resilience.

      Heat shock is a well-established stressor for yeast that elicits a conserved stress response and, most critically, avoids the spore wall permeability problem that complicates the interpretation of trials with chemical stressors. We have actually tried to stress spores with H2O2 at different doses, treatment times, and cell densities; under all tested conditions, spores maintained high viability similar to untreated spores, possibly due to their strong catalase activity (spore suspensions foamed after adding H2O2). Therefore, oxidative stress is not a suitable stressor for the resilient spores. In the revised manuscript, we will show these data in a supplement to justify the use of heat shock as an acute stress condition. 

      Reviewer #2:

      This study is interesting because it provides valuable transcriptomic and proteomic datasets across a large number of conditions in both yeast spores and killifish embryos. Furthermore, these datasets reveal several interesting insights about dormancy, including shared gene expression changes in dormancy across diverse organisms, a trade-off between stress resilience and longevity in yeast spores, and memory of heat stress following exit from dormancy.  This interesting manuscript would be strengthened by additional characterization of the functional consequences of these gene expression changes.

      Major Comments: 

      (1) The finding that a set of gene expression changes in dormancy is shared between yeast spores and killifish diapause embryos is very interesting and raises the question of whether the genetic determinants of stress resilience or longevity in dormant states are also shared. For instance, do any genes identified in the genetic screen as critical for resilience to heat stress in yeast spores also show a similar phenotype in killifish diapause embryos?

      We used the genome-wide deletion library in fission yeast to systematically screen for genes required for spore resilience and longevity. A comparably large-scale functional interrogation is not possible for killifish diapause embryos. Generating deletion-mutant lines for even a single gene in killifish is quite a lengthy and costly effort and would not be realistic within the scope of this work. However, we agree that an exciting direction for future studies is to assess what conserved genes contribute to dormancy function across species.

      (2) The authors show that gene expression changes in dormancy (old vs. young spores and late vs. early diapause) are poorly correlated with aging in stationary-phase yeast cells (Figure 4E) and killifish brain (Figure 5E). The authors suggest that this poor correlation might reflect a lack of aging during dormancy. Interestingly, the authors also show that in both yeast and killifish models, both time and stress in dormancy result in gene expression changes following exit from dormancy. Are these post-dormancy differences correlated with aging in stationary-phase yeast cells and adult killifish? Additionally, could the authors compare these changes with age-related changes in different adult killifish organs?

      We conducted these analyses and did not observe any positive correlation between post-dormancy gene expression changes in yeast offspring from old spores and gene expression changes in stationary-phase ageing cells; if anything, the gene expression signatures showed a slight inverse correlation (R = -0.05; p = 9e-04). For killifish, postdormancy expression changes in embryos from late diapause showed no significant correlations with age-related expression changes in adult brain (R = −0.04, p = 0.84), liver (R = −0.03, p = 0.39), or skin (R = −0.06, p = 0.19). Changes in expression in killifish late diapause compared to early diapause showed no significant correlation with age-related changes in expression in adult killifish liver (R = −0.008, p = 0.53), but did show a weak correlation with old skin (R = 0.18 , p <2e−16), specifically repression of collagen and extracellular matrix genes. We will include the latter analyses in the revised manuscript as supplemental panels.

      (3) In Figures 1C, 4F, and 5F, the authors find significant correlations between two comparisons with a shared condition (e.g. 2-week unstressed spores in Figure 4F). Using the same samples in both comparisons can inflate the correlation between the two sets of fold-changes because the two comparisons are not made independently. The authors should ensure that the correlations found in these figures remain significant even when different samples are used for the shared condition in the two comparisons.

      To address this issue, we re-ran the correlation analyses shown in Figs 1C, 4F and 5F using independent replicates for the shared condition (split-replicate analyses). For example, in Fig 4F, replicate A of the 2-week unstressed spores was used for the old-versus-young comparison, while replicate B was used for the heat-shock-versus-control comparison. For Fig 1C, using independent batches, the correlations remained significant (Batch A vs B: R = −0.16, p = 4.e−16; Batch B vs A: R = −0.27, p <2e−16). For Fig 4F, using independent replicates of the unstressed spores, the correlations remained significant across all replicate pairs (Rep 1 vs Rep 2: R = 0.18, p <2e−16; Rep 2 vs Rep 3: R = 0.14, p = 3e−12; Rep 2 vs Rep 1: R = 0.23, p <2e−16). For Fig 5F, the correlations were also robust across independent pairs (Pair 1: R = 0.15, p = 4e−10; Pair 2: R = 0.13, p = 3e−08; Pair 3: R = 0.16, p = 2e−11). These results show that the correlations are not an artefact of shared samples.

      Minor Comments:

      (4) In Figure 1, it could be informative to present data from intermediate timepoints (i.e., 1, 2, and 5 months). Do mutants that affect spore longevity show a progressive enrichment or depletion over time in dormancy, as depicted in the schematic in Figure 1A? Are there differences in kinetics across the different mutants?

      We thank the reviewer for this suggestion. We will add line plots of the logCPM abundance of short-lived and long-lived mutants (relative to 2-week spore mutants) for all timepoints (2 weeks, 1, 2, 5 and 6 months) to Figure 1. The plots show that most short-lived mutants exhibit progressive depletion over time, while most long-lived mutants exhibit progressive enrichment.

      (5) The authors focus on shared gene expression changes in dormancy between yeast spores, killifish diapause embryos, and dormant cancer cells. Are any pathways enriched among yeast-, killifish-, or human-specific changes?

      We focused on pathways shared across species, but we have now also examined pathways enriched among genes that change specifically in each organism. Among the species-specific gene sets, only genes repressed in yeast spores were significantly enriched, with cytoplasmic translation being the most enriched process (p = 4e-11). The killifish- and human-specific gene sets were not significantly enriched for any functional terms. These results will be included as a supplementary figure.

      Reviewer #3:

      In this study, the authors use several genome-wide approaches to find commonalities and differences between distinct dormancy models, including S. pombe spores, killifish diapause embryos, and human dormant cancer cells. As G0 states are ubiquitous in nature, yet still understudied, this study provides not only a clear demonstration of an evolutionarily-conserved component in G0 regulation, but also a rich resource of genomics datasets, that will be especially of interest to S. pombe and killifish researchers.

      The main conclusions of the study are that: (1) there are commonalities in dormancy programs across eukaryotic evolution; (2) the uncoupling between transcriptome and translatome is more pronounced in non-dividing cells, particularly at ribosomal protein coding genes; and (3) dormant cells retain dynamic responses to stress, changing their cellular state and maintaining this difference.

      Overall, the study is clearly written, provides large data resources for future research in the dormancy field, and the conclusions are supported by the presented evidence (although point 3 would require further experimental clarification, see below).

      Major Comments:  

      A key consideration when comparing transcriptomes of cycling and dormant cells is the overall RNA quantity in the cells; however, it is not made clear in the manuscript whether this overall level was compared and/or if RNA-seq used spike-in quantification. However, this will directly affect some conclusions; for example there is a conceptual difference between a transcript upregulated in spores and a transcript that appears upregulated in spores but is in fact 'less repressed than average' (resulting in higher FC). While the overall transcript diversity as described will be not affected, this analysis can provide important context. For the highlighted example of ribosomal protein mRNAs, this scenario could be what resulted in an apparent mRNA increase but protein decrease (which would be interpreted as both mRNA stabilization and translation inhibition); if the total mRNA is much lower, then the mRNA levels may be similar (which would then be interpreted as no change in transcription nor mRNA stability, but highlight the importance of translation inhibition). This point was more clearly described in a previous seminal study by the authors

      (Marguerat et al 2012), and so should be made clearer here as well. What is the overall RNA level in cycling cells compared to quiescent cells, and compared to spores? Likewise, what is the overall reduction in total protein levels? Was the overall reduction comparable to the previous study? These overall levels should at least be quantified, which is a straightforward experiment. The current mention of this limitation (two sentences in the last paragraph of the conclusion) is not enough. 

      Accordingly, the term "induced" (in Fig 2 for example) may be misleading, and could be separated into truly "induced" genes and "relatively induced" genes, when normalized to absolute levels. Likewise, Fig S2A and S2I are misleading due to this effect (normalization will tend to show a similar number of up-regulated and down-regulated genes). These panels should be clarified by plotting next to the relative quantification (current panels) a comparison taking overall RNA level into account. This will very likely strongly increase the number of downregulated genes and decrease the number of upregulated genes, and the new lists can also be subjected to GO analysis. 

      The same issue would be important to discuss for the killifish experiment as well.

      We acknowledge that the overall quantities of transcripts and proteins in proliferating cells versus those in different dormant cells are important considerations. As described in the Results and Methods, our study presents relative expression changes between dormant and proliferating cells. We highlight this limitation in the Conclusions: “As is the case for most expression studies, our transcriptome and proteome data reflect relative expression levels between two conditions. It is likely that the absolute cellular numbers of most transcripts and proteins are reduced in dormant cells, as they are in quiescent S. pombe cells (Marguerat et al. 2012). Thus, genes induced in dormant cells relative to active cells might be repressed in absolute levels, but to a lesser extent than most other genes.” We also make it clear in the concluding sentences for different sections that our data refer to relative changes (e.g., “Accordingly, several transcripts and proteins were differentially expressed in offspring from 5month-old spores relative to offspring from 2-week-old spores, including 199 mRNAs, 223 proteins, and 33 long non-coding RNAs”). We will carefully review the manuscript, including the Abstract and figures, to further clarify this point, e.g., by using "relatively induced" or “higher relative abundance”. 

      We would like to note, however, that relative quantification, normalised to average expression, focusing on genes that become more or less enriched relative to average genes in the condition of interest compared to a control condition, is a widely accepted and biologically meaningful approach for identifying expression signatures that specify the condition of interest. This is also the principle for qPCR and western analyses, where housekeeping gene normalisation is common. If the absolute expression of all or most genes decreases in dormant cells (which is likely), it would not be meaningful to determine functional enrichments among these genes. Relative expression data help distinguish groups of genes that increase or decrease more than most other genes, reflecting cellular regulation and functional signatures.

      However, we fully agree that the overall quantities of transcripts and proteins in proliferating cells versus those in different dormant cells offer a complementary perspective. Therefore, we are quantifying the overall RNA and protein levels in yeast spores compared to proliferating cells as well as in killifish diapause embryos compared to actively developing embryos. If successful, these results will provide useful context for the relative expression data and will be included in the revised manuscript.  

      For spore offspring stress experiments, the estimated number of cell divisions after spore germination should be indicated. It appears over 20, which would confirm the authors' conclusion that an epigenetic state is induced and persists over multiple rounds of replication and underlie improved heat resistance, indeed hinting at transcriptional memory through chromatin modification. As the phenotype is tested at a population-level, it is unclear whether every spore is equally able to impart this phenotype, and for how long the stress resistance response persists. Moreover, it may be clearer to test heat resistance in log-phase cells, as in the current experimental setup cells are grown to stationary-phase before plating; however, stationary-phase cultures are more heterogenous and more heatresistant.

      We agree, and several of these issues are already addressed in the manuscript. First, regarding the number of cell divisions, the offspring cells were obtained by germinating spores in rich medium and allowing them to grow to mid-exponential phase for approximately 6 generations, as described in the Results (p. 24). Second, for the log-phase testing, we state in the Methods (p. 43) that cultures were adjusted to OD600 = 0.2 and grown to OD600 = 0.6 before stress treatment and plating. Thus, all assays were in fact already performed on log-phase cells.

      We will further emphasise these points in the revised manuscript.

      Yes, our experiments were conducted at the cell population level, and we therefore cannot distinguish whether there is uniform transmission of the transcriptional and phenotypic legacy of dormancy across all germinating spores or whether single cells show heterogeneity within the population in this respect. While this is an interesting question, it would be too technically challenging and time-consuming to pursue this project within the framework of the current study.

      Note also that using 3-fold serial dilutions (instead of more commonly used 10-fold) means that the differences shown in Fig 4M are mild. Plating duplicates (at log-phase) and imaging at several times of growth would be necessary to have a clearer view of the result.

      We fully agree that the serial dilution assay for stress resistance should be improved given the subtle differences. We are repeating the spot assay with 10-fold serial dilutions as suggested, imaging plates at multiple time points, and including sufficient repeats to strengthen the confidence in this result.

      Minor Comments:

      One point for clarification in the introduction is that there is a scale difference between cellular dormancy (at the level of a cell) and organismal dormancy such as diapause (in which the dormancy qualifies the whole organism; typically this will involve slower metabolism, but not necessarily the arrest of the cell cycle for every cell in the organism). As diapause can take different forms depending on organism, detailing this process for the turquoise killifish model may clarify the phenotypic commonalities between these models. Indeed, as pointed out by the authors, several mutants show opposite effects for stationary phase viability and for dormant spore viability, highlighting the diversity of G0 states.

      We agree that there may be a difference between cellular dormancy, in which individual cells arrest their cell cycle, and organismal dormancy, such as diapause, in which individual cells may differ in cell-cycle arrest. However, it has been shown in killifish that all cells throughout the diapause embryo exit the cell cycle and become quiescent (Dolfi et al. 2019), as may be expected given that these embryos can remain dormant for up to two years. We will highlight this aspect more clearly in the Introduction. 

      The title of the manuscript may gain from also mentioning some of the insights (instead of "Gene function and expression of X and Y", "Gene function and expression in X and Y shows Z").

      We agree and will change the title to “Gene function and expression profiling in yeast spores, killifish diapause embryos, and their offspring cells reveal common regulatory features of dormancy

      The Venn diagram in Fig 1D may not be the best way to represent the data; by definition, multiple overlaps here will be zero (between "long-lived spores" and "short-lived spores" and between "long-lived cells" and "short-lived cells"). A table or an upset plot may be useful there. Note that the gained space in Fig 1 may be used to provide examples of mutants in each GO category.

      We agree and will replace the Venn diagram in Figure 1D with an UpSet plot, which better represents the overlapping and non-overlapping categories. 

      Reviewer #4:

      Specific Comments:

      There is a dissonance between the apparent upregulation of autophagy genes during dormancy in both systems and the observation that loss of autophagy genes enhance dormancy. While the authors brush this off by noting "that genes regulated in a given cellular state often do not overlap with those functionally required for that state" (an argument that undermines their whole analysis), it raises the possibility that the process signatures they report are not, in fact, functionally-linked to dormancy and could be coincidental. What would make the expression analysis more convincing would be functional data showing that at least one of the upregulated genes in one of the systems is important for dormancy.

      We respectfully disagree with this assessment. Please note that this apparent paradox between autophagy gene regulation and spore phenotype is highlighted and discussed in the manuscript, and our data actually confirm the importance of autophagy in dormant spores when considered carefully. It is not correct to say that ‘loss of autophagy genes enhance dormancy’. Although autophagy mutants show increased lifespan, they also show decreased stress resiliency. Stress resilience is a key feature and purpose of dormancy, so the loss of autophagy function would clearly be a problem for dormant cells, consistent with the induction of autophagy genes in spores. The surprising trade-off between processes (including autophagy) that support spore longevity and those that support stress resilience is a key finding of our study.

      Regarding the request for functional data linking at least one upregulated gene to dormancy, we note that we have already provided this at a scale far beyond a single gene. In fact, we identified as many as 600 genes that are differentially expressed in spores and, when deleted, cause spore phenotypes (altered stress resilience and/or lifespan). Moreover, the processes significantly enriched among these 600 genes, including protein translation, ribosomes, TCA cycle, and autophagy, are the same processes most enriched among the differentially expressed genes. This significant process-level convergence between functional and expression data provides compelling evidence that our expression signatures are not coincidental but largely reflect roles in dormancy.

      It is not clear how the bar-coding experiment was controlled for mutants that make defective spores (eg poor spore walls) rather than being required for dormancy per se. As the authors note, several of the mutants with the strongest effects (shortening lifespan of the spores) have been previously identified in screens for sporulation or spore wall defects, consistent with those genes playing a specific role in proper spore assembly rather than a general function in dormancy.

      The experimental design was carefully set up to avoid the contribution of mutants that do not sporulate or fail to produce mature spores. After sporulation, samples were heatshocked at 43°C for 3 days and stored in water for at least 2 weeks prior to starting the longevity assay. These conditions will kill any remaining vegetative cells; hence, only spores that can withstand these treatments will be included in the barcode measurements. All abundance values were also normalised to the abundance of each mutant that regrew after two weeks of spore ageing, so any differences in abundance should be caused by our conditions of interest. Thus, mutants with sporulation defects or that fail to form viable spores would be substantially depleted before this normalisation point and would therefore not be included in the subsequent analysis; if they were included, they should not show different abundances across our postsporulation conditions. Nevertheless, we cannot rule out that some of the short-lived mutants reflect problems during sporulation rather than spore traits, as suggested by overlaps with the published sporulation screen. However, this overlap is limited to a very few genes (among 416 short-lived spore mutants), as described in the Results. 

      In this context, we also comment on a broader issue known in the ageing field. Short-lived mutants are less likely to function in ageing-related processes than long-lived mutants, as any perturbation that compromises cell integrity or general fitness – including poor spore wall assembly – will compromise survival regardless of its specific relevance to dormancy regulation. Thus, the long-lived mutants in our screen are more revealing of ageing-related processes. We will clarify and better describe these perspectives in the revised manuscript. 

      It would be helpful to make the data - or at least the lists of genes identified in the screen and the up and downregulated transcripts/proteins available as supplemental information.

      Please note that we provide extensive Supplemental Datasets S1 to S6 that include this very information in table form (genes identified in the Bar-seq screens, differentially expressed RNAs and proteins across the different conditions), along with additional useful data from our large-scale assays. 

      There are at least two different points of diapause in Killifish. It was not entirely clear which diapause was analyzed here. This should be clarified.

      We described the Diapause II stage in the Introduction as the most prominent dormant state and commonly studied state in N. furzeri. In the Results (p. 12), we stated, “To uncover conserved aspects of dormant states between yeast and vertebrates, we analyzed the transcript and protein signatures of N. furzeri diapause II embryos, henceforth called diapause embryos,….”. We also mentioned Diapause II in the Methods. 

      The data that offspring from older or heat-shocked spores are more stress resistant (Figure 4M) is not convincing. Serial dilution assays as shown are more qualitative than quantitative and an actual quantitative measure (eg. c.f.u.'s per cell plated) would be better and would allow statistical confidence to be calculated.

      Serial dilution assays are widely used and accepted to assess differences in stress resistance. The problem with CFUs is that they only determine the proportion of viable cells, while stress resistance also incorporates growth (e.g., stress-sensitive cells may survive the stress but grow more slowly in its presence). However, we agree that the current data in Fig. 4M are not convincing. We are repeating the spot assay with 10-fold serial dilutions, imaging plates at multiple time points, and including sufficient repeats to strengthen the confidence in this result. This is in response to a similar concern raised by Reviewer 3. We will also determine CFU counts, which would provide a more quantitative measure for statistical confidence, should the stress resistance be primarily reflected in cell viability. If necessary, for a more quantitative growth assay allowing statistical analysis, we may also analyse control and stressed cells growing in liquid cultures. 

      Description of the revisions that have already been incorporated in the transferred manuscript

      We have started conducting additional experiments and making text changes to the manuscript, as described in the specific points above. At this stage, we do not provide a partially revised manuscript, but we will provide a fully revised one when everything is ready. 

      Description of analyses that authors prefer not to carry out

      Reviewer 2:

      Comment 1: The finding that a set of gene expression changes in dormancy is shared between yeast spores and killifish diapause embryos is very interesting and raises the question of whether the genetic determinants of stress resilience or longevity in dormant states are also shared. For instance, do any genes identified in the genetic screen as critical for resilience to heat stress in yeast spores also show a similar phenotype in killifish diapause embryos?

      We used the genome-wide deletion library in fission yeast to systematically screen for genes required for spore resilience and longevity. A comparably large-scale functional interrogation is not possible for killifish diapause embryos. Generating deletion-mutant lines for even a single gene in killifish is quite a lengthy and costly effort and would not be realistic within the scope of a revision. However, we agree that an exciting direction for future studies is to assess what conserved genes contribute to dormancy function across species.

    1. eLife Assessment

      The results of this study are important and the approach to dissect the developmental contribution of RIF1 function is convincing. The results that suggest a possibly separate control of replication timing and gene expression are intriguing and represent a strong foundation for future research. The work will be of interest for researchers both in the transcription and the replication field, especially for scientists investigating the interplay between the two processes.

    2. Reviewer #1 (Public review):

      [Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have addressed the comments raised in the previous round of review.]

      The authors sought to determine how Rif1 contributes to DNA replication timing (RT), transcriptional regulation, and embryonic development using zebrafish. They generated a maternal-zygotic rif1 knockout line and examined developmental phenotypes, genome-wide replication timing profiles, RNA-seq, and nascent transcription (SLAM-seq) during early embryogenesis.

      Their major findings in this manuscript are.

      (1) Rif1 is not essential for zebrafish viability, unlike its partially essential role in mice.

      (2) Rif1 deficiency causes defects in female sex determination, delayed epiboly, and reduced primitive erythropoiesis.

      (3) Genome-wide RT is altered by Rif1, but developmental stage has a much larger influence than Rif1 itself.

      (4) Rif1 is required for the proper maturation ("sharpening") of the RT program during development rather than for specific developmental RT switches.

      (5) Rif1 has a much stronger effect on transcription during zygotic genome activation (ZGA) than on replication timing at these early stages.

      (6) Loss of Rif1 leads to increased expression of early zygotic genes, indicating that Rif1 normally suppresses widespread transcription during ZGA.

      Overall, the work proposes that Rif1 independently regulates replication timing and transcription, with these two functions becoming most prominent at different developmental stages.

      The major strengths of the manuscript are as follows.

      (1) the study combines multiple genome-wide approaches including whole-genome RT profiling, RNA-seq, SLAM-seq in combination with gene KO and developmental analyses.

      (2) One of the strongest points is that the authors conducted the analyses at multiple developmental stages rather than a single point.

      (3) The most important conclusion is that the Rif1 regulates transcription during development in a manner largely independent of its RT function, which was further strengthened by the additional data provided in the revised manuscript.

      Comments on revised version:

      The authors responded to my comments in a largely satisfactory manner. They have conducted additional analyses and concluded that Rif1 regulates transcription during ZGA largely independently of its classical RT function, which is an important finding.

      Although authors did not examine origin firing and replication fork rate in rif1 KO cells, which I suggested in my original review, this can be saved for their future studies.

      I think the revised manuscript has been improved and provides important basic information on the functions of the conserved Rif1 protein in RT and transcriptional regulation.

      I have no further recommendation for additional experiments or data analyses.

    3. Reviewer #2 (Public review):

      This study by Masser et al. analyzes global replication timing and gene expression in rif-1 null zebrafish. This work is an extension of their previous report of the normal replication timing pattern during wild-type zebrafish development. The major valuable finding here is that Rif1 is not essential for viability in zebrafish, and - counter to expectation from studies in cultured cells and other species - late replication does not strongly depend on Rif1. Instead, the data suggest that Rif1 subtly sharpens replication timing pattern during normal development rather than function generally to delay replication timing. In the absence of Rif1, the normal pattern establishment is somewhat delayed. The authors also document some changes in expression during development with more genes being repressed by Rif1 than activated at some early stages.

      The study and analysis are generally rigorous, and the conclusions are supported by convincing data. Given the strong link between replication timing and cell type/development, studying timing in a whole developing organism is important. The experimental approach is technically challenging, particularly the bioinformatic analysis. The scientific advance here is largely confined to documenting the timing of Rif1-affected transcription, the unanticipated effect of the rif1 deletion on replication timing and on sex determination, though the latter is not explored. The difference in timing of the transcription phenotypes and replication phenotypes suggests they may be very distinct Rif1 roles. The overall study a useful set of findings and detailed data for future work.

      Loss of Rif1 did not affect viability, but it did strongly influence sex determination, resulting in a lower population of females. This effect is the strongest organismal phenotype, but the study provides no mechanistic explanation for the loss of females from the data gathered here.

      Comments on revised version:

      We are generally satisfied with the revised version of this manuscript.

    4. Author response:

      The following is the authors’ response to the previous reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      The authors sought to determine how Rif1 contributes to DNA replication timing (RT), transcriptional regulation, and embryonic development using zebrafish. They generated a maternal-zygotic rif1 knockout line and examined developmental phenotypes, genome-wide replication timing profiles, RNA-seq, and nascent transcription (SLAM-seq) during early embryogenesis.

      Their major findings in this manuscript are

      (1) Rif1 is not essential for zebrafish viability, unlike its partially essential role in mice.

      (2) Rif1 deficiency causes defects in female sex determination, delayed epiboly, and reduced primitive erythropoiesis.

      (3) Genome-wide RT is altered by Rif1, but developmental stage has a much larger influence than Rif1 itself.

      (4) Rif1 is required for the proper maturation ("sharpening") of the RT program during development rather than for specific developmental RT switches.

      (5) Rif1 has a much stronger effect on transcription during zygotic genome activation (ZGA) than on replication timing at these early stages.

      (6) Loss of Rif1 leads to increased expression of early zygotic genes, indicating that Rif1 normally suppresses widespread transcription during ZGA.

      Overall, the work proposes that Rif1 independently regulates replication timing and transcription, with these two functions becoming most prominent at different developmental stages.

      The major strengths of the manuscript are as follows.

      (1) the study combines multiple genome-wide approaches including whole-genome RT profiling, RNA-seq, SLAM-seq in combination with gene KO and developmental analyses.

      (2) One of the strongest points is that the authors conducted the analyses at multiple developmental stages rather than a single point.

      (3) The most important conclusion is that the Rif1 regulates transcription during development in a manner largely independent of its RT function, which was further strengthened by the additional data provided in the revised manuscript.

      On the other hand, the weakness of the manuscript includes the followings.

      (1) Limited mechanistic insight. The questions such as where Rif1 binds on the chromatin (in relation to the transcriptional promoters/ enhancers and replication origins).

      (2) Which functional domains of RIf1 are involved in regulation of transcription and replication (Is PP1 recruitment required for transcription regulation?) are not addressed.

      (3) Since Rif1 is known to be involved in chromatin organization/ nuclear architecture regulation, the studies addressing this (Hi-C, compartment analyses, ATAC seq etc) would provide important mechanistic information.

      (4) Female sex determination phenotype is intriguing, but it remains largely descriptive, and its mechanisms are elusive at the moment.

      Overall, the results support the authors' conclusions and they have successfully provided answers to the authors' original questions on developmental roles of Rif1 in RT and transcription in vertebrate.

      Comments on revised version:

      The authors responded to my comments in a largely satisfactory manner. They have conducted additional analyses and concluded that Rif1 regulates transcription during ZGA largely independently of its classical RT function, which is an important finding.

      Although authors did not examine origin firing and replication fork rate in rif1 KO cells, which I suggested in my original review, this can be saved for their future studies.

      I think the revised manuscript has been improved and provides important basic information on the functions of the conserved Rif1 protein in RT and transcriptional regulation.

      I have no further recommendation for additional experiments or data analyses.

      We thank the reviewers for their public evaluations of the manuscript.

      Reviewer #2 (Public review):

      This study by Masser et al. analyzes global replication timing and gene expression in rif-1 null zebrafish. This work is an extension of their previous report of the normal replication timing pattern during wild-type zebrafish development. The major valuable finding here is that Rif1 is not essential for viability in zebrafish, and - counter to expectation from studies in cultured cells and other species - late replication does not strongly depend on Rif1. Instead, the data suggest that Rif1 subtly sharpens replication timing pattern during normal development rather than function generally to delay replication timing. In the absence of Rif1, the normal pattern establishment is somewhat delayed. The authors also document some changes in expression during development with more genes being repressed by Rif1 than activated at some early stages.

      The study and analysis are generally rigorous, and the conclusions are supported by convincing data. Given the strong link between replication timing and cell type/development, studying timing in a whole developing organism is important. The experimental approach is technically challenging, particularly the bioinformatic analysis. The scientific advance here is largely confined to documenting the timing of Rif1-affected transcription, the unanticipated effect of the rif1 deletion on replication timing and on sex determination, though the latter is not explored. The difference in timing of the transcription phenotypes and replication phenotypes suggests they may be very distinct Rif1 roles. The overall study a useful set of findings and detailed data for future work.

      Loss of Rif1 did not affect viability, but it did strongly influence sex determination, resulting in a lower population of females. This effect is the strongest organismal phenotype, but the study provides no mechanistic explanation for the loss of females from the data gathered here.

      Comments on revised version:

      We are generally satisfied with the revised version of this manuscript.

      We thank the reviewers for their public evaluations of the manuscript.

    1. eLife Assessment

      This important study explores the function of SIRT2 in inhibiting Japanese encephalitis virus infection and disease progression in rodent models. The findings presented are novel as sirtuins are known for their roles in aging, metabolism, and cell survival, but have not been studied in the context of viral infections until recently. The evidence supporting the claims is solid and reveals a mechanism for SIRT2 in modulating acetylated NF-kB transcription factor levels and additional inflammatory response mediators. The work will be of interest to biologists studying viruses, sirtuins, and inflammation.

    2. Reviewer #1 (Public review):

      Summary:

      Desingu et al. show that JEV infection reduces SIRT2 expression. Upon JEV infection, 10-day old SIRT2 KO mice showed increased viral titer, more severe clinical outcomes, and reduced survival. Conversely SIRT2 overexpression reduced viral titer, clinical outcomes and improved survival. Transcriptional profiling show dysregulation of NF-KB and expression of inflammatory cytokines. Pharmacological NF-KB inhibition reduced viral titer. The authors conclude that SIRT2 is a regulator of JEV infection.

      Strengths:

      This paper is novel because sirtuins have been primarily studied for aging, metabolism, stem cells/regeneration. Their role in infection has not been explored until recently. Indeed, Barthez et al. showed that SIRT2 protects aged mice from SARS-CoV-2 infection (Barthez, Cell Reports 2025). Therefore, this is a timely and novel research topic. Mechanistically, the authors showed that SIRT2 suppresses the NF-KB pathway. Interesting, SIRT2 has also been shown recently to suppress other major inflammatory pathways, such as cGAS-STING (Barthez, Cell Reports 2025) and the NLRP3 inflammasome (He, Cell Metabolism 2020; Luo, Cell Reports 2019). Together, these findings support the emerging concept that SIRT2 is a master regulator of inflammation.

      Comments on revised version.

      The authors have addressed my comments.

    3. Reviewer #2 (Public review):

      The manuscript by Desingu et al., explores the role of SIRT2 in regulating Japanese Encephalitis Virus (JEV) replication and disease progression in rodent models. Using both an in vitro and an in vivo approach, the authors demonstrate that JEV infection leads to decreased SIRT2 expression, which they hypothesize is exploited by JEV for viral replication. To test this hypothesis, the authors utilize SIRT2 inhibition (via AGK2 or genetic knockout) and demonstrate that it leads to increased viral load and worsens clinical outcomes in JEV-infected mice. Conversely, SIRT2 overexpression via an AAV delivery system reduces viral replication and improves survival among infected mice. The study proposes a mechanism in which SIRT2 suppresses JEV-induced autophagy and inflammation by deacetylating NF-κB, thereby reducing Beclin-1 expression (an NF-κB-dependent gene) and autophagy, which the authors consider a pathway that JEV exploits for replication. Transcriptomic analysis further supports that SIRT2 deficiency leads to NF-κB-driven cytokine hyperactivation. Additionally, pharmacological inhibition of NF-κB using Bay 11 (an IKK inhibitor) results in reduced viral load and improved clinical pathology in WT and SIRT2 KO mice. Overall, the findings from Desingu et al. are generally supported by the data and suggest that targeting SIRT2 may serve as a promising therapeutic approach for JEV infection and potentially other RNA viruses that SIRT2 helps control.

      Comments on revised version.

      We believe the authors of Desingu et al. have adequately addressed the major concerns of each reviewer. Thus, we believe the current version of the manuscript is significantly improved, and the data more strongly support the authors' conclusions.

      We would like to point out that the statistics in Figure 8 are difficult to interpret and should be improved to make them useful to the reader and strengthen the paper. Also, line 70 has an extra period that should be removed.

    4. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      Desingu et al. show that JEV infection reduces SIRT2 expression. Upon JEV infection, 10-day-old SIRT2 KO mice showed increased viral titer, more severe clinical outcomes, and reduced survival. Conversely, SIRT2 overexpression reduced viral titer, clinical outcomes, and improved survival. Transcriptional profiling shows dysregulation of NF-KB and expression of inflammatory cytokines. Pharmacological NF-KB inhibition reduced viral titer. The authors conclude that SIRT2 is a regulator of JEV infection.

      This paper is novel because sirtuins have been primarily studied for aging, metabolism, stem cells/regeneration. Their role in infection has not been explored until recently. Indeed, Barthez et al. showed that SIRT2 protects aged mice from SARS-CoV-2 infection (Barthez, Cell Reports 2025). Therefore, this is a timely and novel research topic. Mechanistically, the authors showed that SIRT2 suppresses the NF-KB pathway. Interestingly, SIRT2 has also been shown recently to suppress other major inflammatory pathways, such as cGAS-STING (Barthez, Cell Reports 2025) and the NLRP3 inflammasome (He, Cell Metabolism 2020; Luo, Cell Reports 2019). Together, these findings support the emerging concept that SIRT2 is a master regulator of inflammation.

      We thank the reviewer for his/her valuable time, clear summary, and critical comments on the work.

      Weaknesses:

      (1) Figures 2 and 3. Although SIRT2 KO mice showed increased viral titer, more severe clinical outcomes, and reduced survival upon JEV infection, the difference is modest because even WT mice exhibited very severe disease at this viral dose. The authors should perform the experiment using a sub-lethal viral dose for WT mice, to allow the assessment of increased clinical outcomes and reduced survival in KO mice.

      We thank the reviewer for the valuable recommendation. We conducted an experiment using a sub-lethal viral dose of 5 × 10^2 PFU in 14-day-old wild-type (WT) and SIRT2 knockout (KO) mice to evaluate clinical outcomes. Our findings showed a significant reduction in survival rates among the SIRT2-KO mice compared to the wild-type mice (Figure 2O).

      (2) Figure 5K-N, the authors examined the expression of inflammatory cytokines in WT and SIRT2 KO cells upon JEV infection, in line with the dysregulation of NF-kB. It has been shown recently that SIRT2 also regulates the cGAS-STING pathway (Barthez, Cell Reports 2025) and the NLRP3 inflammasome (He, Cell Metabolism 2020; Luo, Cell Reports 2019). Do you also observe increased IFNb, IL1b, and IL18 in SIRT2 KO cells upon JEV infection? This may indicate that SIRT2 regulates systemic inflammatory responses and represents a potent protection upon viral infection. This is particularly important because in Figure 7F, the authors showed that SIRT2 overexpression reduced viral load even when NF-KB is inhibited, suggesting that NF-KB is not the only mediator of SIRT2 to suppress viral infection.

      We agree with the suggestion made by the reviewers. We have quantified the transcript levels of IFNb, IL1b, and IL18 and found that the expression of these genes was increased in JEV-infected SIRT2-KO mice compared to wild-type mice (Figure S5D-S5F). Additionally, we acknowledge the reviewers' comments regarding the role of SIRT2 in regulating systemic inflammatory responses and its significant protective effect against viral infections. Furthermore, NF-κB is not the only mediator involved in SIRT2's suppression of viral infections; other molecular mechanisms may also contribute to this process.

      Reviewer #2 (Public review):

      The manuscript by Desingu et al., explores the role of SIRT2 in regulating Japanese Encephalitis Virus (JEV) replication and disease progression in rodent models. Using both an in vitro and an in vivo approach, the authors demonstrate that JEV infection leads to decreased SIRT2 expression, which they hypothesize is exploited by JEV for viral replication. To test this hypothesis, the authors utilize SIRT2 inhibition (via AGK2 or genetic knockout) and demonstrate that it leads to increased viral load and worsens clinical outcomes in JEV-infected mice. Conversely, SIRT2 overexpression via an AAV delivery system reduces viral replication and improves survival among infected mice. The study proposes a mechanism in which SIRT2 suppresses JEV-induced autophagy and inflammation by deacetylating NF-κB, thereby reducing Beclin-1 expression (an NF-κB-dependent gene) and autophagy, which the authors consider a pathway that JEV exploits for replication. Transcriptomic analysis further supports that SIRT2 deficiency leads to NF-κB-driven cytokine hyperactivation. Additionally, pharmacological inhibition of NF-κB using Bay 11 (an IKK inhibitor) results in reduced viral load and improved clinical pathology in WT and SIRT2 KO mice. Overall, the findings from Desingu et al. are generally supported by the data and suggest that targeting SIRT2 may serve as a promising therapeutic approach for JEV infection and potentially other RNA viruses that SIRT2 helps control. However, the paper does fall short in some areas. Please see below for our comments to help improve the paper.

      We thank the reviewer's valuable recommendation. We measured NF-kB acetylation in JEV-infected Neuro-2a cells treated with Ad-null or Ad-SIRT2. Our findings show that levels of NF-kB acetylation and JEV-NS5 were reduced in the SIRT2-overexpressing cells (Figure S6).

      We conducted an experiment using a sub-lethal viral dose of 5 × 10^2 PFU in 14-day-old wild-type (WT) and SIRT2 knockout (KO) mice to evaluate clinical outcomes. Our findings showed a significant reduction in survival rates among the SIRT2-KO mice compared to the wild-type mice (Figure 2O).

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      In Figures 5C-F, the text is very small.

      Thanks for raising this point. We have increased the text size in the revised Figures 5C-F

      Reviewer #2 (Recommendations for the authors):

      Major concerns:

      (1) Line 71: The introduction does not adequately cite existing work demonstrating the role of SIRT2 in viral infections, including other RNA viruses. It would be more effective to summarize the current literature (similar to lines 162-165) rather than stating that the role of SIRT2 in RNA virus infection is largely unknown.

      We thank the reviewer for the valuable suggestion to improve the manuscript substantially. We have cited existing work demonstrating the role of SIRT2 in viral infections, including other RNA viruses, in the introduction, as suggested.

      (2) Figure 1C: The image should be quantified to state that the observation is statistically significant.

      Thank you for the valuable suggestion. We have quantified the proteins SIRT2, LC3 I, LC3 II, p62, and Beclin in Mock- and JEV-infected Neuro-2a cells, as well as in mouse brain Western blots. These results, along with representative blots (Figure 6I-6J) and n-number-related blots, are presented graphically along with p-values in supplementary figures 7A-7B. Our analysis indicates that these proteins are expressed at statistically significant levels.

      (3) Figure 1I: What tissue is 1I? not stated in the manuscript text or the figure legend.

      Thank you for pointing that out. These samples are from mouse brains. We have included this information in both the text and the figure legend of the revised manuscript.

      (4) Figure 2A: How does Sirt2 expression in WT-infected cells compare to the levels observed in the KD? Are they at comparable levels?

      Thank you for highlighting an important point. We used control siRNA to compare SIRT2 knockdown in SIRT2-siRNA-treated cells in JEV infection. In Figure 2A, “control siRNA” was labelled as “control.” In the revised Figure 2A, we updated this to read “control siRNA.”

      (5) Figure 2L-N: Clinical outcome comparing WT and SIRT2KO mice, JEV-infected mice seems modest. Can statistical analysis be performed to determine whether the results are statistically significant?

      We agree with the suggestion. Clinical signs (Figure 2L) and paralysis (Figure 2M) onset were earlier in SIRT2-KO mice compared to WT mice, and the survival curve (Figure 2N) shows statistical significance. We conducted an experiment using a sub-lethal viral dose of 5 × 10^2 PFU in 14-day-old wild-type (WT) and SIRT2 knockout (KO) mice to evaluate clinical outcomes. Our findings showed a significant reduction in survival rates among the SIRT2-KO mice compared to the wild-type mice (Figure 2O).

      (6) Figure 3: AGK2 can also inhibit other Sirtuins. Thus, to test the contribution of other Sirtuins, the experiment could be repeated using wild-type and Sirt2 KO mice.

      We agree with the suggestion. We repeated the experiment using wild-type and SIRT2-KO mice and found that the differences in survival rates were statistically insignificant between JEV-infected wild-type mice treated with AGK2 and JEV-infected SIRT2-KO mice treated with AGK2 (Figure 3K).

      (7) Figure 5: A-B: Clearly label diagrams and explain what is being compared. G-I: Please annotate genes in the heat maps and volcano plots.

      Thank you for pointing out an important aspect. The figures include several genes that may not be easily visible at readable levels in the main figure. To address this, we have enlarged the figures and labelled them with the gene names in a clearer format, as shown in the supplementary Figures S2-S4. Additionally, we have updated the figure legend for Figure 5 to include this information.

      (8) Line 230-232: "Taken together, these findings reveal that JEV infection negatively regulates SIRT2 levels, resulting in a concomitant increase in acetylated NF-κB mediated virus replication". Data does not support this statement. The experiments presented do not demonstrate that acetylated NF-kB mediates viral replication. In fact, it would be good to measure NF-kB acetylation in AdSIRT2 JEV-infected cells compared to WT-infected cells, to verify that the acetylation of NF-kB is truly linked to SIRT2 expression levels.

      We thank the reviewer for his/her valuable recommendation. We measured NF-kB acetylation in JEV-infected Neuro-2a cells treated with Ad-null or Ad-SIRT2. Our findings show that levels of NF-kB acetylation and JEV-NS5 were reduced in the SIRT2-overexpressing cells (Figure S6).

      (9) Figure 6E: The Graph is hard to interpret. It is also missing a figure legend/key, and also a misplaced A in the image.

      Thank you for highlighting an important point. We have highlighted NF-κB and other colour representations in the signalling network graph (Figure 6E). We revised the figure legend for clarity and removed the misplaced "A" from the figure.

      (10) Line 260-261: "These results indicate that SIRT2 regulates JEV replication through the NF-κB-beclin-autophagy axis.". Data does not support this statement.

      We modified the statement as follows: “These results suggest that SIRT2 regulates JEV replication through the NF-κB-autophagy axis.

      (11) Line 270: Do the authors mean decrease? Bay 11 is an NF-κB inhibitor.

      We agree with the suggestion. This is a typographical error. We have replaced “increase” with “decrease” in the revised manuscript.

      (12) Figure 8: The phenotype seems modest. Have the authors considered that Sirt2 may regulate JEV via other acylation targets?

      We agree with the reviewers' suggestion. NF-κB is not the only mediator involved in SIRT2's suppression of viral infections; other molecular mechanisms may also contribute to this process.

    1. eLife Assessment

      The characterization of a dissociable Mediator subunit implicated in cellular pathways, particularly lung alveolar function and HIV latency, would be conceptually interesting. The authors have preliminary evidence for a Med16 subcomplex that may regulate specific genes. This work is useful in that it points to interactions between Med16 and UBP1, but the evidence is somewhat preliminary and incomplete.

    2. Reviewer #1 (Public review):

      This work reports the finding of a dissociable human Mediator complex subunit, MED16, and its recruitment of transcription factors UBP1 and TFCP2 that form a trimeric complex regulating cellular gene transcription and inhibiting HIV promoter activity, thus promoting MED16-targeting inhibitors as HIV latency-reversing agents for anti-HIV therapy. In this revision, the authors provide new data indicating that MED16 action occurs in the holo-Mediator complex, not as a free form, for its functional association with UBP1 and TFCP2.

      Summary:

      Characterization of a dissociable Mediator subunit implicated in cellular pathways, particularly lung alveolar function, and HIV latency is conceptually interesting.

      Strengths:

      The strengths of this study are:

      (1) Demonstration of MED16 dissociation from the core Mediator complex and formation of a subcomplex containing MED16, upstream-binding protein 1 (UBP1), and transcription factor cellular promoter 2 (TFCP2) by elegant biochemical fractionation and immunoblotting analysis.

      (2) Defining nine N-terminal WD-40 repeats (WDRs) of MED16 as a Mediator-incorporating module and the C-terminal ⍺β-domain (157 amino acids) important for interaction with the UBP1-TFCP2 heterodimeric complex.

      (3) Illustration of a weak hydrophobic interaction between MED16 and the Mediator core that could be disrupted by 1,6-hexanediol, but not by its 2,5-hexanediol isomer nor by high salt (500 mM NaCl) disruption.

      (4) Classification of UBP1-upregulated cellular genes typically containing binding sites flanking the transcription start site (TSS) in contrast to UBP1-downregulated genes often containing a TSS-overlapping UBP1-binding site;

      (5) Presenting evidence for Mediator complex-dissociated free MED16-repressed HIV promoter activity through functional association with UBP1 and showing bromodomain-containing protein 4 (BRD4) inhibitor JQ1 that potentially disrupts BRD4-inhibited HIV-1 transcription elongation could lead to reversal of HIV-1 latency.

      Weaknesses:

      (1) No clear demonstration that MED16-UBP1-TFCP2 indeed form a trimeric core subcomplex in regulating cellular gene transcription and HIV-1 promoter inhibition.

      (2) No validation of transcriptomic datasets and pathways identified.

      (3) Use of mostly artificial reporter gene constructs and non-HIV host cells (e.g., human 293T embryotic kidney cells, human HeLa cervical cancer cells, and mouse HT pancreatic cancer cells) for examining MED16/UBP1-regulated HIV transcription.

      (4) Inconsistent use of 293T and HeLa cells in the characterization of dissociated MED16 interaction with UBP1 and TFCP2.

      (5) In vitro transcription using immobilized DNA templates was not performed to a high standard thus failing to convincingly show MED16/UBP1-inhibited HIV-1 transcription preinitiation complex formation.

      In this revision, the authors stipulate that the action of MED16-UBP1-TFCP2 is through the holo-Mediator complex, not as a free trimeric complex, since deletion of the N-terminal Mediator-incorporating module abolishes the functional effect of MED16 even though its interaction with UBP1-TFCP2 persists. The authors also show that a predicted UBP1-binding site is indeed present in the promoter region not overlapping with the TSS in each of the UBP1/MED16-upregulated respiratory gaseous exchange genes, such as surfactant protein A1 (SFTPA1), SFTPA2, SFTPB and SFTPC. To demonstrate UBP1/MED16-inhibited HIV transcription in a more biologically relevant system, the authors employ an HIV-1 latency model derived from CD4+ T cells. Additional experiments are also performed to address other issues raised by the reviewers.

      Overall, the revised manuscript has been improved by addressing some of the reviewer's comments. Additional issues that came up while reading this revision are: a) The statement, "UBP1 (as known as LBP-1) and TFCP2 (as known as LSF) belong to a subfamily of the TFCP2/Grainyhead transcription factor family" (lines 63-64), is confusing and needs to be clarified. My understanding is that LBP-1 has four family members encoded by two different genes (see Yoon et al., MCB 14: 1776-1785, 1994): LBP-1a and LBP-1b are probably UBP1, and LBP-1c and LBP-1d are likely TFCP2/LSF. If this is correct, the authors should clearly indicate which specific isoforms are analyzed in their UBP1 and TFCP2 proteins and clarify some of the text descriptions seemingly contradictory to the earlier findings.

      The use of mostly artificial reporter templates and transfection assays conducted mostly in 293T and HeLa cells remains concerning. It is also unclear why overexpression of MED16 only, but not MED23 or MED24, inhibits HIV promoter activity (Figure 5) if MED16 action is through the holo-Mediator complex and overexpression of MED16 may titrate out key cellular factors, thus diminishing their action through holo-Mediator. Results based on this squelching type of experiment relying on overexpression of a single component needs to be carefully interpreted.

    3. Reviewer #2 (Public review):

      Summary:

      The article from Zheng et al. proposes that the Med16 subunit of Mediator can dissociate from the complex, associate with transcription factor UBP1, and contribute to activation or repression of specific sets of genes in human cells. The question is potentially interesting, and the possibility of a distinct role for Med16 in UBP1-dependent transcription could be of interest to the field. However, I have substantial concerns about the experimental design, reproducibility, and interpretation of the data. In particular, several of the central conclusions rely on single experiments, indirect assays, or perturbations that are expected to cause broader changes in Mediator structure and transcription. In my view, the current data therefore do not provide sufficient evidence to support several of the principal mechanistic conclusions of the manuscript.

      Strengths:

      The authors provide preliminary evidence that Med16 may exist in an altered or partially dissociated Mediator complex and may be associated with specific transcriptional effects. The use of biochemical assays, reporter assays, RNA-seq, and Med16 knockout/rescue experiments provides several potentially useful approaches to investigating this question. In particular, the Med16 knockout/rescue RNA-seq experiment described in the rebuttal is a potentially informative experiment. Making the underlying IP-MS abundance and peptide-count data available is also an improvement over the previous version.

      However, important limitations of these experiments need to be addressed before the proposed mechanism can be established.

      Comments on the revised version:

      In the previous review, I raised concerns that the evidence for a specific UBP1-Med16 interaction and for a mechanistic role of this interaction was limited. The revised manuscript does not resolve these concerns. In particular, whether UBP1 interacts specifically with Med16, whether this interaction is required for activation of UBP1 target genes, and whether UBP1 represses HIV-1 transcription through Med16 remain unresolved.

      A stronger case for a specific UBP1-Med16 interaction would require more direct biochemical, structural, or biophysical evidence. The current experiments do not distinguish sufficiently between a specific Med16-dependent mechanism and broader consequences of perturbing Mediator structure or transcription.

      The revision also does not include the additional biological replicates needed to assess the reproducibility of several foundational experiments. Consequently, a number of the central conclusions remain based on single experiments, which substantially limits the strength of the conclusions that can be drawn.

      Major concerns:

      Figure 1:

      All of the data in Figure 1 are from single experiments without biological replicates. Because Figure 1 provides an important foundation for the proposed mechanism, this limits confidence in the reproducibility of the observations. Western blot experiments can show substantial experiment-to-experiment variability, and such variability appears relevant to several of the observations presented here.

      A key experiment is the western blot analysis of the gel-filtration experiment in Figure 1D. Rather than migrating with Mediator, the majority of the detected Med16 appears to elute at a lower molecular weight. The extract was maintained at super-physiological salt concentrations from preparation through column loading and elution, which could potentially contribute to this result. Other possibilities include experiment-to-experiment variation or detection of a cross-reacting protein by the Med16 antibody. The latter possibility warrants consideration because the bands detected in fractions 28-31 appear to migrate at a different molecular weight from the Med16 bands in fractions 16-19. Additional replicated experiments and appropriate controls would be needed to establish that the lower-molecular-weight signal represents Med16 in a distinct complex.

      Figure 2:

      The data in Figure 2 are also based on single experiments without biological replicates. The biochemical experiments use protein fragments that do not occur as such in the native protein. Such experiments can be useful for mapping possible protein-protein interaction surfaces, but they can also generate false-positive or false-negative interactions depending on the experimental configuration.

      Some of the results raise additional questions about the proposed interaction model. For example, the observations involving TFCP2 in panel B and Med1 and Med23 in panel F appear difficult to reconcile with a simple model in which UBP1 interacts with Mediator through Med16. Panel 2F is particularly relevant because Med1 and Med23 are not detected despite the proposed model involving UBP1 interaction with Mediator. These observations should be addressed when interpreting the specificity and mechanism of the proposed UBP1-Med16 interaction.

      Figure 3:

      The reporter assays provide potentially useful preliminary evidence for factors that influence transcription. However, the relatively long experimental timeframes (36-48 hours after transfection), use of artificial promoters, and overexpression of proteins introduce important limitations.

      The data are consistent with the authors' model, but they are also consistent with a simpler explanation in which Med16 depletion broadly affects Mediator structure and function. Previous work from the Gross, Robert, and Roeder laboratories has shown that loss of Med16 can result in dissociation of Med23, Med24, and Med25 from Mediator. Importantly, the authors' own data in the rebuttal document appear to show similar effects following Med16 loss. These observations are important because Med23, Med24, and Med25 provide major interfaces for DNA-binding transcription factors, and Med16, Med23, Med24, and Med25 together represent a substantial fraction of the molecular mass of Mediator. The functional properties and stability of the resulting Mediator complex therefore need to be considered when interpreting Med16 depletion experiments.

      The authors' data also suggest broader transcriptional effects: Med16 knockout causes loss of basal transcription in panel 3E. Conversely, overexpression of Med16 or Med16 fragments can cause broad defects in transcriptional activation, including through squelching. The relatively modest effects observed despite substantial increases in protein abundance are therefore also consistent with non-specific consequences of overexpression. These alternative explanations need to be considered when interpreting the reporter assays.

      Figure 4:

      RNA-seq is an appropriate approach for investigating possible effects of Med16 on gene expression. However, the experimental design has important limitations because Med16 depletion is expected to affect the composition of Mediator, including loss of Med23, Med24, and Med25. The knockdown also occurs over an extended timeframe (>24 hours), making it difficult to distinguish direct effects of Med16 from secondary effects arising from altered Mediator structure or broader changes in transcription.

      The RNA-seq results could therefore reflect Med16-specific functions, but they are also consistent with a model in which loss of Med16 compromises Mediator structure and function and produces downstream gene-expression changes.

      The presentation of the RNA-seq data in Figure 4E is also difficult to interpret. The volcano plot shows UBP1 overexpression versus the shCTRL vector, whereas the comparison central to the authors' interpretation concerns the effects of shMed16. Showing the volcano plot for the shMed16 comparison would allow readers to assess the statistical significance and magnitude of the relevant gene-expression changes directly. Instead, the authors superimpose "x" symbols for genes that "failed to be activated by UBP1 in the shMed16 cell line." Because the underlying Med16 comparison is not displayed, the p-values and fold changes for the comparison of interest cannot be evaluated. Moreover, positioning the "x" symbols according to the coordinates of the UBP1-overexpression volcano plot does not appear to represent their corresponding positions in a volcano plot of the Med16 comparison. This makes the figure difficult to interpret and could give a misleading visual impression of the underlying data.

      Finally, the RNA-seq data are not spike-in normalized. This is potentially important because Med16 depletion is expected to cause broad effects on transcription and could result in an overall reduction in transcription across the genome. Appropriate normalization is therefore important for distinguishing gene-specific effects from global changes. I also did not identify information in the manuscript indicating where the RNA-seq data have been deposited in a public repository.

      Figure 5:

      The reporter assays in Figure 5 are subject to similar concerns. The authors' model may explain the observations, but the data are also consistent with broader disruption of Mediator structure and function following Med16 depletion, or with more general transcriptional effects caused by overexpression of Med16 or Med16 fragments.

      A separate concern is the proposed mechanism for HIV-1 repression. The authors propose that UBP1 interacts with Med16/Mediator and that overexpression of Med16 enhances Mediator recruitment, although Mediator recruitment is not directly measured. The proposed increase in Mediator recruitment would, in isolation, be expected to promote transcription rather than repression. The authors instead invoke a UBP1-dependent mechanism to explain the repression. However, a simpler explanation is that overexpression of Med16 or selected Med16 fragments perturbs normal transcription through mechanisms including squelching. The authors themselves invoke a Med16 squelching model to explain reduced transcription at the UBP1 reporter in Figure 3G-I. The possibility that the same mechanism contributes to the results in Figure 5 should therefore be addressed.

      Finally, a central component of the proposed model is recruitment of Mediator by UBP1 through Med16, but neither UBP1 occupancy nor Mediator occupancy at the relevant promoters is directly measured. These experiments would provide important evidence for the proposed mechanism. There is also no legend for Figure 5G,H, and the legend for Figure 5C,D appears to be incorrect.

      Figure 6:

      The title of Figure 6 states that "The position of the UBP1 binding site relative to the TSS dictates whether MED16-UBP1 activates or represses transcription." The data presented do not establish this conclusion.

      Only one alternative location of the UBP1 binding site is tested, and the analysis in panel D compares only the 50 most up- or down-regulated genes in UBP1-overexpression experiments. The correlations shown are relatively weak, and a rigorous statistical analysis is not presented.

      The authors cite Duttke et al. (Nature 2024) in support of the concept that transcription-factor position can influence regulatory activity. That study used genome-wide mapping of native TSSs, MPRA analysis of approximately 2,000 promoter variants, and targeted follow-up experiments. By contrast, Figure 6 relies on an artificial reporter and a single change in UBP1 binding-site position. The current data therefore support the possibility that binding-site position influences the observed reporter activity, but they do not establish the broader conclusion that the position of the UBP1 binding site relative to the TSS dictates whether Med16-UBP1 activates or represses transcription.

      Figure 7:

      The title of Figure 7 states that Med16 "interferes with HIV-1 PIC assembly," but the data presented do not establish this mechanism.

      First, the proposed mechanism is described as acting through interaction with UBP1, but UBP1 is not measured in this experiment.

      Second, Med16 knockout or overexpression has relatively modest effects on the estimated levels of bound PIC components, and these changes do not consistently correspond to the transcriptional changes shown in panel C. For example, in the WT versus Med16 KO comparison, there is no apparent difference in the levels of Pol II, Med1, IIB, or IIH based on the western blot data. The reduction in Med16 and Med23 is expected following Med16 knockout and therefore does not by itself establish altered PIC assembly. In the WT versus Med16 overexpression comparison, panel C suggests a meaningful reduction in transcription, whereas the changes in PIC-associated proteins do not follow the direction predicted by the proposed model: Pol II and IIB increase with Med16 overexpression, while Med1, Med23, Med16, and IIH appear similar.

      Third, Cdk8 and Med12 are not PIC components. They are subunits of the CDK8 module, which can inhibit PIC formation. Even under a simplified interpretation in which reduced Cdk8 and Med12 levels were relevant to PIC formation, the direction of these changes is opposite to that expected from the "relative mRNA level" plots in panel C. Reduced levels of these proteins in the Med16 overexpression condition would, under this interpretation, be expected to promote rather than reduce transcription.

      Finally, this is a single-replicate experiment, and quantitative comparisons are made across immobilized-template pulldowns using different nuclear extracts (WT, Med16 KO, and Med16 OE). Differences in protein concentration between extracts, differences in pulldown efficiency, and experiment-to-experiment variation could all contribute to the observed differences. Replicated experiments with appropriate normalization would therefore be needed to support the proposed PIC mechanism.

      Figure 8:

      Panel F presents a model that, in my view, goes beyond what is supported by the experimental data. In particular, there is currently insufficient evidence to establish the proposed Med16-UBP1-TFCP2 complex as a distinct functional complex.

      The model also depicts the Mediator CDK8 module bound at promoters together with Pol II. The manuscript does not provide evidence supporting this arrangement, and there is substantial prior evidence that the CDK8 module can inhibit or prevent PIC formation. This aspect of the model therefore requires either experimental support or clarification.

      Finally, the model depicts a separate Med16-UBP1-TFCP2 complex. This appears inconsistent with the authors' statements in the rebuttal that they did not intend to propose such an unsupported isolated complex. If this is not the intended model, Figure 8 should be revised to avoid conveying that interpretation.

      Figure S1: IP-MS analysis:

      The IP-MS data raise substantial concerns regarding the specificity of the proposed UBP1-Med16 interaction.

      The authors state that they "have now reanalyzed the IP-MS data using a more precise quantification approach." However, the filtering procedure described in the Methods is difficult to evaluate and requires substantially more justification. The procedure includes multiple sequential criteria: detection in at least two of three replicates of the target bait; a minimum unique peptide count; detection in at least one additional Mediator bait; exclusion of Mediator/Pol II subunits, ribosomal proteins, histones, keratins, and common contaminants; and annotation with transcriptional or DNA-binding functions using GO terms. The rationale for this particular combination of filters and its impact on the candidate list should be explained and justified.

      The resulting "bait-preferential score" is also difficult to assess. The reported mean_target and mean_other values are frequently negative, with some positive values, and the method appears to derive a positive score from the difference between these values after taking an absolute value. The biological and statistical interpretation of this score is therefore unclear. The reported scores are generally clustered around relatively narrow values, and the analysis does not appear to provide the statistical framework normally used to assess enrichment and reproducibility in IP-MS experiments.

      Importantly, the candidate proteins identified by this approach do not appear to overlap with proteins identified in other published Med16 IP-MS analyses in human cell lines, including HCT116, MCF7, HEK293T, and HeLa. I was unable to identify a protein among the 37 Med16 candidates that was consistently observed in the cited resources, including BioPlex (Huttlin et al., Cell 2021) and Malovannaya et al. (Cell 2011). This lack of concordance raises questions about the specificity and physiological relevance of the proposed Med16-associated proteins and, in particular, about the evidence for a specific UBP1-Med16 interaction.

      The peptide counts for UBP1 also illustrate the limitation of the current analysis. The total UBP1 peptide counts across the Cdk8, Med1, and Med16 IP-MS experiments are 12, 10, and 14, respectively, yielding bait-preferential scores of 1.07, 0.76, and 1.85. Given these relatively small differences and the absence of statistical significance measures, it is difficult to determine whether these observations provide evidence for a specific biological interaction.

      The authors have now made the raw abundance values and peptide counts available as supplementary tables, which is useful. However, standard statistical measures for evaluating MS data, including FDR, p-values, and adjusted p-values, are not provided, nor is sufficient information given about how the reported scores were calculated. These additions would be important for readers to assess the robustness of the conclusions.

      Concerns about the rebuttal document:

      Several statements in the rebuttal appear inconsistent with the data shown in the revised manuscript or with the interpretation of the authors' own experiments. I highlight specific examples below.

      (1) The proposed Med16-UBP1 complex:

      I previously raised concerns that the manuscript overstated the evidence for a separate Med16 complex associated with UBP1 and lacked rigorous biochemical characterization of such a complex. The authors responded that the wording of the original manuscript may have created this interpretation and stated that this was not the mechanistic model they intended to convey. However, Figure 8 of the revised manuscript still depicts a separate Med16-UBP1-TFCP2 complex. The figure therefore appears to retain the interpretation that the authors stated they did not intend to convey. This should be clarified.

      (2) Replication and experimental rigor:

      I previously raised concerns about the absence of replication and the limitations of the experimental approaches used to infer roles for Med16 and UBP1. These concerns remain because no additional replicate experiments were performed for several of the key experiments. As a result, important conclusions continue to depend on single western blots, including foundational experiments in Figures 1, 2, and 7.<br /> Examples include the variable quantification in Figure 1A; the absence of detectable Med23 and Med1 in the UBP1 IP in Figure 2F despite the proposed interaction between UBP1 and Mediator; and the variable association of TFCP2 with Flag-MED16 fragments in Figure 2B, including lanes 7-9. Replication would be important for determining whether these observations are reproducible.

      (3) IP-MS analysis:

      The revised IP-MS analysis provides additional information compared with the previous version, but important concerns remain regarding the processing and interpretation of the data. The "bait-preferential score" approach is not sufficiently justified, and standard measures such as p-values, adjusted p-values, or FDR are not provided.

      The analysis also identifies no proteins among the 37 Med16 candidates that appear to be consistently observed in other published Med16 IP-MS analyses in human cell lines. This is important when assessing the specificity of the proposed interaction.

      The peptide counts for UBP1 are similarly low and do not show a clear Med16-specific enrichment: 12 peptides in the Cdk8 IP, 10 in the Med1 IP, and 14 in the Med16 IP, corresponding to scores of 1.07, 0.76, and 1.85, respectively. These observations do not by themselves provide strong evidence for a specific UBP1-Med16 interaction.<br /> In addition, the Med16 IP experiments identify approximately 800 proteins per replicate. This broad set of associated proteins highlights the importance of appropriate controls and statistical analysis when interpreting a co-IP as evidence for a specific protein-protein interaction.

      (4) Interpretation of the 1,6-HD experiments:

      I previously raised concerns that the IP-western data in Figure 1 did not establish a Med16-specific effect. The rebuttal states that "MED16 exhibits a uniquely pronounced dissociation by 1,6-HD that is consistent across three independent IPs using anti-CDK8, anti-MED1, and anti-MED12 antibodies." However, the quantitative data shown do not clearly support this interpretation.

      In the CDK8 IP, CDK8 and MED23 appear to be lost to a similar extent as Med16. In the Med1 IP, Med12 shows a similar loss to Med16, with Med23 also showing a comparable trend. In the Med12 IP, CDK8 and Med23 show changes comparable to Med16. There are also unexplained increases in some Mediator subunits with increasing 1,6-HD concentrations. For example, Med12 and Med1 are variable across the 1,6-HD conditions, and Med1 and Med24 increase at higher concentrations. Similar trends are present in the CDK8 IP.

      The Figure 1B data raise related concerns. Several Mediator subunits show substantial increases following the 2,5-HD condition compared with 1,6-HD, in some cases greater than the change observed for Med16. Thus, the available data do not clearly establish that Med16 exhibits a uniquely pronounced dissociation from Mediator.

      (5) Evidence for the proposed mechanism:

      I previously suggested that more rigorous approaches would be needed to distinguish a specific Med16-dependent mechanism from broader effects of Med16 perturbation. The rebuttal instead emphasizes that the authors' conclusion that the UBP1-TFCP2 heterodimer acts through Med16 is supported by multiple independent lines of evidence.

      However, the different experiments do not independently establish the same mechanistic conclusion. Many rely on long experimental timeframes, overexpression, reporter assays, limited controls, or lack of biological replication. These approaches can provide useful preliminary evidence for effects on transcription but do not establish that a specific UBP1-Med16 interaction is required for UBP1 target-gene expression.

      Additional approaches that directly test the proposed interaction and its requirement for transcriptional regulation would therefore be needed to establish this mechanism.

      (6) Med16 rescue experiment:

      The authors performed an RNA-seq comparison of WT, Med16 KO, and Med16 KO cells rescued by re-expression of WT Med16. This is a potentially informative experiment, and I appreciate that it was performed.

      However, the heatmap shown in the rebuttal does not appear to demonstrate a clear rescue of UBP1 target-gene expression. The figure contains approximately 60 genes but provides no legend beyond "Gene profiling of MED16 KO and rescue effect." The authors describe the result as showing that re-expression of Med16 can largely restore expression of UBP1 target genes, but this conclusion is not readily apparent from the figure as presented. More information about the analysis, normalization, and comparisons would be needed to evaluate the claimed rescue. It would also be useful to include the relevant analysis in the revised manuscript rather than only in the rebuttal.

      (7) Consequences of Med16 depletion:

      A continuing concern is that the manuscript does not adequately account for the broader consequences of Med16 depletion or knockout. Loss of Med16 is known to cause dissociation of other Mediator subunits, including Med23, Med24, and Med25. The authors acknowledge this in the rebuttal and state that their new Co-IP and IP-MS experiments confirm partial dissociation of these subunits following Med16 knockout. They also acknowledge that this structural perturbation must be considered when interpreting phenotypes from Med16 depletion and that long-term knockdown or knockout approaches are susceptible to secondary effects and cellular adaptation.<br /> These are important caveats, but they are not adequately incorporated into the interpretation of the revised manuscript, and the new data demonstrating loss of Med23, Med24, and Med25 are not shown in the article.

      This issue is particularly important for interpreting the transcriptional experiments. Loss of Med16 is expected to disrupt Mediator more broadly, including loss of Med23, Med24, and Med25, which provide important transcription-factor interaction surfaces. These changes could produce widespread transcriptional effects independently of a specific UBP1-Med16 mechanism. Given that the authors' own experiments now provide evidence for this structural perturbation, the possibility of indirect effects and altered Mediator function should be explicitly incorporated into the experimental interpretation.

      Overall assessment:

      The manuscript addresses an interesting question and presents several potentially useful observations concerning Med16, Mediator, and UBP1. However, the central mechanistic conclusions are currently not supported by sufficiently rigorous or independent evidence. The principal concerns are the extensive reliance on single-replicate experiments; the use of overexpression, artificial reporters, and long depletion timeframes; the absence of direct measurements of UBP1 and Mediator occupancy; the broad structural consequences of Med16 depletion; and the limited statistical support for the IP-MS analysis.

      Taken together, the current data do not distinguish adequately between the proposed specific UBP1-Med16 mechanism and alternative explanations involving broader disruption of Mediator structure or transcription. Addressing this distinction is important because it affects the interpretation of much of the manuscript, including the RNA-seq and reporter-assay results and the proposed HIV-1 mechanism.

      The authors have generated several useful datasets, including the Med16 knockout/rescue experiment and additional IP-MS information, but the manuscript would need to incorporate the associated caveats and provide stronger experimental evidence before the proposed mechanistic model can be established.

    4. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      Characterization of a dissociable Mediator subunit implicated in cellular pathways, particularly lung alveolar function, and HIV latency is conceptually interesting.

      Strengths:

      The strengths of this study are:

      (1) Demonstration of MED16 dissociation from the core Mediator complex and formation of a subcomplex containing MED16, upstream-binding protein 1 (UBP1), and transcription factor cellular promoter 2 (TFCP2) by elegant biochemical fractionation and immunoblotting analysis.

      (2) Defining nine N-terminal WD-40 repeats (WDRs) of MED16 as a Mediator-incorporating module and the C-terminal ⍺β-domain (157 amino acids) important for interaction with the UBP1-TFCP2 heterodimeric complex.

      (3) Illustration of a weak hydrophobic interaction between MED16 and the Mediator core that could be disrupted by 1,6-hexanediol, but not by its 2,5-hexanediol isomer nor by high salt (500 mM NaCl) disruption.

      (4) Classification of UBP1-upregulated cellular genes typically containing binding sites flanking the transcription start site (TSS) in contrast to UBP1-downregulated genes often containing a TSS-overlapping UBP1-binding site

      (5) Presenting evidence for Mediator complex-dissociated free MED16-repressed HIV promoter activity through functional association with UBP1 and showing bromodomain-containing protein 4 (BRD4) inhibitor JQ1 that potentially disrupts BRD4-inhibited HIV-1 transcription elongation could lead to reversal of HIV-1 latency.

      Weaknesses:

      Nevertheless, foreseeable weaknesses include:

      (1) No clear demonstration of MED16-UBP1-TFCP2 indeed forming a trimeric core subcomplex in regulating cellular gene transcription and HIV-1 promoter inhibition

      We appreciate this insightful comment from the reviewer. The physical interaction between MED16 and the UBP1–TFCP2 heterodimer was uncovered serendipitously via biochemical fractionation. Specifically, UBP1 and TFCP2 were identified in gel-filtration fractions containing free MED16 that eluted separately from the intact core Mediator complex (Figures 1D–1F). This biochemical observation suggested to us a potential functional linkage between MED16 and these two transcription factors. Subsequently, our functional assays further clarified this regulatory relationship: UBP1 and TFCP2 do interact with MED16 that is required to be fully assembled into Mediator complexes to exert transcriptional control. Truncation of MED16’s N-terminal WD40-repeat (WDR) domain (MED16ΔWDR) eliminates MED16 integration into the Mediator complex. Notably, this MED16ΔWDR mutant still retains an intact C-terminal αβ-domain capable of binding UBP1 yet completely loses the capacity to support UBP1-dependent transcription. Collectively, these data demonstrate that UBP1–TFCP2 engage MED16 as a component of the complete Mediator complex to govern target gene expression in cells.

      We have updated the abstract and full manuscript to present this model more clearly and coherently throughout the text.

      (2) No validation of transcriptomic datasets and pathways identified.

      We thank the reviewer for this comment. Since our RNA-seq analysis identified SFTP family genes (SFTPA1, SFTPA2, SFTPB, SFTPD) as UBP1-MED16 co-regulated targets, and these are lung-specific surfactant protein genes, we have now performed qRT-PCR validation in the A549 human lung adenocarcinoma cell line. The results confirm that knockdown of MED16 largely attenuates the expression of these SFTP genes (see Author response image 1) and other UBP1 target genes identified in our RNA-seq dataset.

      Author response image 1.

      qRT-PCR validation of SFTP gene family expression (SFTPA1/A2/B/D) in A549 cells

      (3) Use of mostly artificial reporter gene constructs and non-HIV host cells (e.g., human 293T embryonic kidney cells, human HeLa cervical cancer cells, and mouse HT pancreatic cancer cells) for examining MED16/UBP1-regulated HIV transcription.

      To overcome the limitations of artificial reporter systems, we have managed to utilize multiple complementary approaches beyond reporter assays, including: (i) RNA-seq analysis of endogenous gene regulation, (ii) biochemical co-IP and gel filtration experiments, (iii) immobilized template transcription assays that directly measure PIC assembly and nascent RNA production, and (iv) validation in the J-Lat 10.6 CD4+ T cell line harboring an integrated latent HIV-1 provirus, where MED16 overexpression suppresses HIV-1 reactivation.

      Unfortunately, the direct infection experiments with replication-competent HIV-1 were not feasible as our institution does not have the biosafety level 3 (BSL-3) containment facilities.

      (4) Inconsistent use of 293T and HeLa cells in the characterization of dissociated MED16 interaction with UBP1 and TFCP2.

      For biochemical characterization in this study, HEK293T cells were primarily utilized for overexpression-based co-immunoprecipitation (Co-IP) and domain-mapping assays owing to their superior transfection efficiency. In contrast, HeLa cells were employed for gel-filtration chromatography and endogenous Co-IP, as fractionation of HeLa nuclear extracts represents a well-established workflow for purifying native protein complexes. Critically, the physical interaction between MED16 and the UBP1–TFCP2 heterodimer was consistently detected in both HEK293T whole-cell lysates and HeLa nuclear extracts across all assays.

      (5) In vitro transcription using immobilized DNA templates was not performed to a high standard, thus failing to convincingly show MED16/UBP1-inhibited HIV-1 transcription preinitiation complex formation.

      We appreciate the reviewer’s careful assessment of the immobilized template assay. While we recognize the technical challenges inherent in this approach, we want to emphasize that the proposed model—that MED16-UBP1 inhibits HIV-1 transcription at the level of PIC assembly—does not rest solely on this single experiment.

      Multiple independent lines of evidence converge on this model:

      (1) The MED16-UBP1 inhibition depends on the UBP1 binding site overlapping the TSS: a chimeric reporter with the UBP1 binding site placed at the TSS is inhibited by MED16, whereas placing it upstream does not show inhibition, demonstrating the UBP1 binding site-specific repression (Fig 6A-C).

      (2) MED16 overexpression inhibits HIV-1 reporter activity in a manner depending on interaction with UBP1. Deletion of the MED16 αβ-domain (MED16Δαβ), which abolishes MED16-UBP1 interaction, completely abrogates the inhibitory effect (Fig 5E-G).

      (3) The MED16-UBP1 inhibition of HIV is independent of TAR-mediated elongation (Fig S2B-C) and HDAC-mediated deacetylation (Fig S2A), consistent with an inhibition mechanism operating at the initiation stage rather than at the post-initiation steps.

      (4) Upon MED16 overexpression, the immobilized template assay shows reduced CDK8 binding and Pol II pSer5 signals, along with TFIIB retention, providing mechanistic evidence that is consistent with the aforementioned functional observations.

      Thus, the model is supported by convergent evidence from reporter assays, domain-mapping experiments, pharmacological inhibition, and in vitro biochemical reconstitution. In the revised manuscript, we have further clarified how these complementary approaches collectively support the PIC inhibition model.

      Reviewer #2 (Public review):

      Summary:

      The article from Zheng et al. proposes an interesting hypothesis that the Med16 subunit of Mediator detaches from the complex, associates with transcription factor UBP1, and this complex activates or represses specific sets of genes in human cells. Despite my excitement upon reading the abstract, I was concerned by the lack of rigor in the experimental design. The only statement in the abstract that has some experimental support is the finding that Med16 dissociates from the Mediator and forms a subcomplex, but the data shown remain incomplete.

      We would like to first address a core conceptual point that we believe underpins several of the reviewer’s concerns.

      We respectfully disagreed that the reviewer summarized our proposed model as a pathway where “Med16 detaches from the complex, associates with transcription factor UBP1, and this complex activates or represses specific sets of genes.” We acknowledge that phrasing in our original manuscript may have created this misleading interpretation, yet this is not the mechanistic model we intend to convey. We identified the MED16–UBP1–TFCP2 interaction serendipitously during biochemical fractionation: UBP1 and TFCP2 co-eluted with free MED16 in gel-filtration fractions that were fully separated from the intact core Mediator complex. While this biochemical result guided our subsequent functional investigation into MED16–UBP1–TFCP2 crosstalk, we never intend to propose that a standalone MED16–UBP1–TFCP2 subcomplex can drive transcriptional activation or repression independently of the intact Mediator complex.

      Most importantly, our functional experiments demonstrate that UBP1 and TFCP2 require MED16 as an integral part of the Mediator complex to regulate transcription as shown by domain mapping experiments. Specifically, MED16 uses its N-terminal WDR domain to integrate into the Mediator and its C-terminal αβ-domain to bind UBP1/TFCP2. Deletion of either WDR or αβ-domain is functionally inert in both cellular and HIV-1 reporter assays. Thus, the MED16-UBP1 interaction is functionally relevant because it links these transcription factors to the Mediator complex, not because it forms an autonomous regulatory complex. In another word, UBP1–TFCP2 target MED16 to recruit the whole Mediator complex for governing gene expression in cells.

      We have comprehensively revised the full manuscript to sharply distinguish our initial biochemical observation from the validated functional model. As all experimental approaches carry inherent limitations, we tried our best to validate every major conclusion using multiple orthogonal assays and to draw mechanistic interpretations when results from distinct experimental systems do converge. Our detailed replies to each specific concern from the reviewer are provided below.

      Strengths:

      The authors have preliminary evidence that a stable Med16 complex may exist and that it may regulate specific sets of genes.

      Weaknesses:

      The experiments are poorly designed and can only infer possible roles for Med16 or UBP1 at this point. Furthermore, the data are often of poor quality and lack replication and quantitation. In other cases, key data such as MS results aren't even shown. Instead, we are given a curated list of only about 6 proteins (Figure S1), a subset of which the authors chose to pursue with follow-up experiments. This is not the expected level of scientific process.

      As clarified in our Summary Response above, we thought this reviewer may have misinterpreted our data and model. Overall, our work demonstrates that MED16 acts as a molecular bridge, linking UBP1/TFCP2 to the Mediator complex, not that three proteins form a free-standing subcomplex, and we have extensively revised the manuscript to clarify this.

      Here we also want to address the specific concerns about data completeness and rigor. The full mass spectrometry datasets were not clearly presented in the original submission. We have now reanalyzed the IP-MS data using a more precise quantification approach. Rather than scoring proteins by simple presence or absence, we calculated a bait-preferential score for each protein by comparing its normalized abundance in the MED16 IP to that in the other three Mediator bait IPs (MED1, MED12, CDK8). This score reflects the degree to which a protein is preferentially enriched by MED16 relative to other Mediator subunits. The bait-specific candidates identified by this analysis are presented in Fig S1B, and the complete list of MED16-specific candidates is now included. In addition, the raw abundance values and peptide counts for all identified proteins have been compiled and uploaded as supplementary tables. A detailed description of the normalization and scoring procedure is provided in the Methods section

      Furthermore, we have added quantification analysis for key Western blot results throughout the revised figures.

      (1) The data supporting the Med16 dissociation and co-association with UBP1 are incomplete and not convincing at this stage. According to the Methods and text, the gel filtration column was run with "un-dialyzed HeLa cell nuclear extract" and eluted in 300mM KCl buffer. The extracts were generated with the Dignam/Roeder method according to the text. Undialyzed, that means the extract would be between 0.4 - 0.5M NaCl. Under these high salt conditions (not physiological), it's possible and even plausible that Mediator subunits could separate over time. This caveat is not mentioned or controlled for by the authors. Because a putative Med16 subcomplex is a foundational point of the article, this is concerning.

      The data are incomplete because a potential Med16 complex is not defined biochemically. The current state suggests a smaller Med16-containing complex that may also contain UBP1 and other factors, but its composition is not determined. This is important because if you're going to conclude a new and biologically relevant Med16 complex, which is a point of the article, then readers will expect you to do that.

      We appreciate the reviewer’s thorough feedback and address the two core concerns separately below.

      (1) Undialyzed nuclear extracts & high-salt conditions

      Published work confirms Mediator undergoes massive precipitation (~70% loss) upon dialysis to low-salt 100 mM KCl buffer (1), justifying our use of undialyzed HeLa nuclear extracts for biochemical assays. Prior study also demonstrated intact Mediator remains stable in up to 1 M KCl (1). Therefore, MED16 dissociation seen at 300 mM KCl arises from its unique biochemical property, rather than non-specific high-salt damage.

      (2) Biochemical interpretation of MED16-containing fractions

      We have revised ambiguous original wording to clarify our model: we do not intend to propose an autonomous MED16–UBP1–TFCP2 subcomplex functions independently in cells. Gel filtration only captures a biochemical state where MED16 co-elutes with UBP1/TFCP2 separate from core Mediator, which merely inspired our functional follow-up. Three lines of evidence prove UBP1/TFCP2 require fully assembled Mediator to function:

      (i) MED16’s N-terminal WDR domain mediates Mediator integration, while its C-terminal αβ-domain binds UBP1; both interactions can occur simultaneously (Fig 2B).

      (ii) MED16Δαβ incorporates into Mediator but loses UBP1 binding, showing no activity in UBP1-driven and HIV-1 reporter assays (Fig 3G, 5G).

      (iii) MED16ΔWDR retains UBP1 binding yet cannot assemble into Mediator, thus fails to repress HIV-1 transcription (Fig 5F).

      In vitro MED16 separation only reflects its extractable biochemical behavior, not a functional standalone complex. Collectively, our data establish UBP1/TFCP2 recruit the entire Mediator complex via MED16. All relevant manuscript sections have been rewritten to distinguish this preliminary biochemical observation from our validated functional model.

      Equally concerning are the IP-western results shown in Figure 1. In my opinion, these experiments do nothing to support the claims of the authors. The authors use hexanediols at 5% or 10% in an effort to disrupt the Mediator complex. Assuming this was weight/volume, that means ~400 to 800mM hexanediol solution, which is fairly high and can be expected to disrupt protein complexes, but the effects haven't been carefully assessed as far as I'm aware. The 2,5 HD (Figure 1B) experiments appear to simply contain greater protein loading, and this may contribute to the apparent differential results. In fact, in looking at the data, it seems that all MED subunits probed show the same trend as Med16. They are all reduced in the 1,6HD experiment relative to the 2,5 HD experiment. But it's hard to know, because replicates weren't completed and quantitation was not done. There aren't even loading controls. Other concerns about the IP-Western experiments are outlined in point 2.

      Treatment with 10% (w/v) 1,6-hexanediol (1,6-HD) is a well-established method to selectively disrupt weak hydrophobic interactions while maintaining stable protein assemblies. A comparable strategy was recently applied to dissect NEAT1-dependent paraspeckle formation driven by liquid–liquid phase separation (2). Notably, we included 2,5-hexanediol (2,5-HD) as a critical specificity control: as a structural isomer with distinct hydroxyl group positioning, 2,5-HD cannot efficiently disrupt weak hydrophobic contacts and failed to trigger MED16 dissociation (Figure 1B). The divergent outcomes observed side-by-side with equal sample input therefore serve as an internal control, validating that MED16 dissociation is a specific consequence of 1,6-HD treatment.

      We acknowledge mild signal reductions for several other Mediator subunits following 1,6-HD exposure under certain immunoprecipitation (IP) conditions (Figures 1A, 1B). Nevertheless, MED16 exhibits a uniquely pronounced dissociation by 1,6-HD that is consistent across three independent IPs using anti-CDK8, anti-MED1, and anti-MED12 antibodies, in both HEK293T whole-cell lysates and HeLa nuclear extracts. By contrast, the mild depletion of other Mediator subunits varies substantially depending on the antibody and cell extract used, whereas robust MED16 loss is universally observed in all experimental setups we tested.

      (2) At no point do the authors apply rigorous methods to test their hypothesis. Instead, methods are applied that have been largely discredited over time and can only serve as preliminary data for pilot studies, and cannot be used to draw definitive conclusions about protein function.

      We greatly appreciate the reviewer’s emphasis on methodological rigor. We fully concur that individual techniques including protein fractionation, IP-WB, transient transfection, shRNA knockdown, immobilized template and luciferase reporter assays each carry intrinsic limitations when deployed alone. To address this, we employed a panel of orthogonal experimental strategies throughout our work and only draw conclusions supported by convergent results across distinct assays. Our core mechanistic conclusion—that MED16 acts as a molecular bridge linking the UBP1–TFCP2 heterodimer to the intact Mediator complex to control transcription—is validated by multiple independent lines of evidence, rather than resting on a single experimental approach, as elaborated in detail below.

      (a) IP-westerns are fraught with caveats, especially the way they were performed here, in which the beads were washed at relatively low salt and then eluted by boiling the beads in loading buffer. This will "elute" bound proteins, but also proteins that non-specifically interact with or precipitate on the beads. And because Westerns are so sensitive, it is easy to generate positive results. It's just not a rigorous experiment.

      We acknowledge that IP-Western has inherent limitations regarding specificity. To minimize non-specific signals, we included IgG controls in our CDK8 IP experiments (available in raw data) and the boiling elution method has been extensively validated in our hands. More importantly, the interaction between MED16 and UBP1/TFCP2 is not solely supported by IP-Western.

      This interaction was initially observed through co-elution of MED16 with UBP1 and TFCP2 in gel filtration fractions (Fig 1D), confirmed by reciprocal co-IP with anti-MED16 and anti-TFCP2 antibodies in both HeLa NE and the resolved fractions (Fig 1E-F), and further corroborated by in vitro GST pull-down assays showing direct binding between MED16 αβ-domain and recombinant UBP1 (Fig 2C). IP-MS analysis with three biological replicates independently identified UBP1 and TFCP2 as MED16-associated proteins. Each of these methods has different sources of potential artifacts, yet they all converge on the same conclusion.

      (b) Many conclusions relied on transient transfection experiments, which are problematic because they require long timeframes, during which secondary/indirect effects from expression/overexpression will result. This is especially true if the proteins being artificially expressed/overexpressed are major transcription regulators, which is the case here. It is simply impossible to separate direct from indirect effects with these types of experiments. Another concern is that there was no effort to assess whether the induced protein levels were near physiological levels. Protein overexpression, especially if the protein is a known regulator of pol2 transcription (e.g., UBP1 or Med16), will create many unintended consequences.

      The reviewer raises a valid concern about indirect effects from overexpression. To address this, our key conclusions were validated by multiple loss-of-function approaches that do not rely on overexpression alone. Specifically, the conclusion that MED16 is required for UBP1 transcriptional activity is supported by:

      (i) shRNA-mediated MED16 knockdown (Fig 3C);

      (ii) CRISPR-mediated MED16 knockout in five independent frameshift clones with consistent phenotypes (Fig 3E-F);

      (iii) Using a domain-disruption approach, we demonstrated that a UBP1-binding-deficient MED16 mutant (Δαβ) fails to exert the dominant-negative effect on UBP1 activity, as seen with wild-type MED16 (Fig 3G and 6A). All these different methods point to the same conclusion. This makes it unlikely that the effects we see are simply artifacts of overexpression or indirect consequences.

      (c) Many conclusions were made based upon shRNA knockdown experiments, which are problematic because they require long timeframes (see above point), which makes it nearly impossible to identify effects that are direct vs. indirect/secondary/tertiary effects. Also, shRNA experiments will have off-target effects, which have been widely reported for well over a decade. An advantage of shRNA knockdowns is that they prevent genetic adaptation (a caveat with KO cell lines). A minimal test would be to show phenotypic rescue of the knockdown by expressing a knockdown-resistant Med16 (for example), but these types of experiments were not done.

      We agree that shRNA has potential off-target effects, and that is why we did not rely on shRNA as our sole loss-of-function approach. We have generated five independent CRISPR MED16 knockout clones, each with a distinct frameshift mutation, and all five clones showed consistent reduction in UBP1 reporter activity (Fig 3E-F). Furthermore, we performed RNA-seq on MED16 knockout cells in which wild-type MED16 was re-introduced. As presented in Author response image 2, re-expressing MED16 in KO cells can largely restore the expression of UBP1 target genes. These rescue data provide strong evidence that the transcriptional defects are specifically attributable to MED16 loss rather than off-target effects or clonal adaptation.

      Author response image 2.

      Gene profiling of MED16 KO and rescue effect

      (d) Many experiments used reporter assays, which involved artificial, non-native promoters. Reporters are good for pilot studies, but they aren't a rigorous test of direct regulatory roles for Med16 or other proteins. Reporters don't even measure transcription directly. In fact, no experiment in this study directly measures transcription. An RNA-seq experiment was done with overexpressed or Med16 knockdown cells, but these required long timeframes and RNA-seq measures steady-state mRNA, which doesn't test the potential direct effects of these proteins on nascent transcription.

      The two principal reporters used in this study are both based on native promoters. The UBP1 reporter is derived from the mouse α-globin gene promoter, a well-established natural target of UBP1/TFCP2 that contains two endogenous UBP1 binding sites. To enhance sensitivity, we expanded these to six copies, as the α-globin promoter has low basal activity in most cell lines. Critically, this expansion preserved the original promoter architecture, including the CCAAT box, TATA box, and TSS. The HIV-1 reporter is driven by the HIV-1 core promoter (-78 to +60), which contains all elements required for basal transcription and Tat-mediated activation. Thus, both reporters retain the native sequence context of the regulatory elements under study.

      These reporter assays served as initial screening and hypothesis-testing tools. The key functional insights from reporters were then validated in more physiological contexts:

      (i) The position-dependent effect of UBP1 binding (activation when upstream of TSS, repression when overlapping TSS) observed in chimeric reporters (Fig 6A-C) was independently confirmed by motif enrichment analysis of endogenous genes from RNA-seq data (Fig 6D-E).

      (ii) Our immobilized template transcription assays directly measure nascent RNA production from the HIV-1 promoter, showing that MED16 overexpression reduces transcriptional output (Fig 7C). Thus, while reporter assays alone would be insufficient, our conclusion on UBP1-MED16 function are further corroborated by both global genomic analysis and direct in vitro transcription measurements.

      (e) The MS experiments show promise, but the data were not shown, so it's hard to judge. The reader cannot compare/contrast the experiments, and we have no indication of the statistical confidence of the proteins identified. How many biological replicate MS experiments were performed?

      The IP-MS experiments were performed with three biological replicates for each of the four antibodies against MED1, MED12, CDK8, and MED16 respectively. The complete datasets are now provided in the revised manuscript (Supplementary Table 1).

      In addition, we have reanalyzed the data using a more quantitative approach. In the original submission, we classified proteins by a binary cutoff—detected or not detected in MED16 relative to other baits. In the revised analysis, we calculated a bait-preferential score for each protein by comparing its normalized abundance in the MED16 IP to its mean abundance across the other three Mediator baits. This score reflects how strongly a protein is enriched by MED16, rather than simply whether it meets an arbitrary detection threshold. A detailed description of this method is provided in the revised Methods section.

      Notably, using this quantitative method, UBP1 and TFCP2 can also be detected at lower levels in some replicates of the other Mediator baits. This is consistent with our functional model: UBP1 and TFCP2 associate with the whole Mediator complex through MED16. The MED16-biased enrichment pattern, now supported by the bait-preferential score, reinforces the conclusion that MED16 serves as the primary anchor for UBP1/TFCP2 to associate with the Mediator complex.

      (3) The data are over-interpreted, and alternative (and more plausible) hypotheses are ignored. Many examples of this, some of which are alluded to in the points above. For example, Med16 loss or overexpression will cause compensatory responses in cells. An expected result is that Mediator composition will be disrupted, since Med16 directly interacts with several other subunits. Also in yeast, the Robert, Gross, and Morse labs showed that loss of Med16/Sin4 causes loss of other tail module subunits, and this would be expected to cause major changes in the transcriptome. The authors also mention that yeast Med16/Sin4 "alters chromatin accessibility globally" and this would be expected to cause major changes in the transcriptome, leading to unintended consequences that will make data analysis and identification of direct Med16 effects impossible. The unintended consequences will be magnified with prolonged disruption of MED16 levels in cells (e.g., longer than 4h). These unintended consequences are hard to predict or define, and are likely to be widespread given the pivotal role of Mediator in gene expression. One unintended consequence appears to be loss of pol2 upon Med16 over-expression, as suggested by the western blot in Figure 8B. I point this out as just one example of the caveats/pitfalls associated with long-term knockdowns or over-expression.

      We thank the reviewer for raising this important point. Indeed, our new Co-IP and IP-MS data confirm that MED16 knockout leads to partial dissociation of MED23, MED24, and MED25 from the Mediator complex. We agree that this structural perturbation must be carefully considered when interpreting phenotypes from MED16 depletion experiments, and we acknowledge that long-term knockdown or knockout approaches are inherently susceptible to secondary effects and cellular adaptation.

      Author response image 3.

      MED16 knockout impacts on the Mediator complex

      However, our key conclusions are not based solely on long-term depletion experiments. Several lines of evidence, including experiments performed on short timescales without prolonged MED16 loss, converge on the same conclusion:

      First, the MED16 mutant lacking its C-terminal αβ-domain cleanly separates the two functions. This mutant retains the intact N-terminal WDR domain of MED16 and can integrate into the Mediator complex, preserving its structural role and preventing tail module dissociation. Yet it completely fails to exert the competitive inhibition (squelching) effect on the UBP1 reporter that is observed with wild-type MED16 overexpression (Fig 3G), and similarly fails to inhibit HIV-1 reporter activity (Fig 5G). The sole molecular defect of this mutant is its inability to bind UBP1 (Fig 2B). Critically, this overexpression experiment is performed in a wild-type background where the endogenous Mediator complex remains structurally intact. The only variable is whether the overexpressed MED16 can interact with UBP1. This provides strong evidence that the transcriptional effects are mediated specifically through the MED16-UBP1 interaction interface, independent of MED16's structural role in maintaining Mediator integrity.

      Second, the transcriptional changes we observe are not random but show specific functional coherence. If the transcriptomic changes in MED16 knockdown cells were merely secondary consequences of Mediator tail module disruption, we would expect broad, non-specific dysregulation. Instead, the UBP1-MED16 co-regulated genes are enriched for specific biological processes—angiogenesis—which largely align with the known biological functions of UBP1 (Fig 4D-E). This functional specificity is difficult to reconcile with the global, non-specific transcriptional disruption that would be expected from the total Mediator dysfunction.

      Finally, we wish to address the reviewer's observation regarding Pol II levels in Figure 8B. In preparing the revised manuscript, we carefully re-examined our original Western blot data and found that the reduction of Pol II upon MED16 overexpression was not consistently reproducible across independent experiments. We have therefore replaced new Pol II blot in Figure 8B. We note that this revision does not affect any of the conclusions drawn from Figure 8, which focus on the inhibitory effect of MED16 overexpression on HIV-1 latency reversal in J-Lat cells.

      We are grateful for the reviewer’s careful assessment, which encouraged us to refine our data presentation and enhance experimental rigor. The manuscript has been significantly improved as a result.

      Reviewer #3 (Public review):

      Weaknesses:

      (1) The decrease in 1,6-hexanediol-treated cells of MED16 is modest, variable, not quantified, and internally inconsistent. For example, in Figure 1A, 1,6-hexanediol treatment should not have an impact on the level of the protein being directly IP. For MED12 (and CDK8 and MED1 to a lesser extent), 1,6-hexanediol treatment alters the level of the target protein in the IP. Along these lines, Figure 1A shows a no 1,6H-D dependent decrease in MED1 or MED12 levels in the CDK8 IP, whereas Figure 1B does show a decrease. Figure 1A shows no 1,6H-D dependent decrease in CDK8 levels in the MED1 IP, whereas Figure 1B shows a dramatic decrease. MED24 levels in the MED12 IP increase upon 1,6H-D in Figure 1A, but decrease in Figure 1B. Internal inconsistencies of this nature persist in the other Figures.

      We thank the reviewer for this careful observation, and we here clarify the rationale behind our experimental design to resolve the apparent discrepancies between Figures 1A and 1B.

      We first performed co-immunoprecipitation (co-IP) assays using HEK293T whole-cell lysates with three distinct antibodies targeting CDK8, MED1, and MED12, respectively, to pull down intact Mediator complexes. This setup was designed to assess whether 1,6-hexanediol (1,6-HD) impairs Mediator complex integrity and, if so, whether this perturbation affects individual subunits unequally. Across all three independent IP replicates, we observed a uniform trend: most Mediator subunits displayed only mild signal attenuation, whereas MED16 exhibited a striking, dose-dependent reduction upon escalating 1,6-HD concentrations (Fig 1A). Quantitative densitometry of these immunoblot signals has now been incorporated into the revised Figures 1A and 1B to better visualize this difference. Thus, this initial result in Fig 1A indicated that MED16’s interaction with the core Mediator complex is uniquely vulnerable to 1,6-HD disruption, irrespective of which Mediator subunit was used for immunoprecipitation.

      To validate the reproducibility of this phenotype, we replicated the assay in an alternative biochemical system using HeLa nuclear extracts (NEs) (Fig 1B), with 2,5-hexanediol (2,5-HD) included as a critical negative control—this structural isomer cannot disrupt weak hydrophobic protein-protein contacts. Notably, the HeLa NE experiments in Fig 1B were carried out under elevated salt conditions (~300 mM NaCl), in contrast to the 150 mM NaCl buffer used for the HEK293T lysate assays in Figure 1A.

      We want to emphasize that despite these substantial technical disparities between the two experimental setups, both datasets converge on the identical core conclusion: MED16’s association with the core Mediator complex is selectively disrupted by 1,6-HD. This consistent trend is further strengthened by quantitative analysis of immunoblot band intensities from biological replicates, which we have added to the revised figures.

      Minor variability observed for other Mediator subunits between Fig 1A and 1B arises from intrinsic differences in cell type, extraction protocols, and buffer salt concentrations. For this reason, we did not draw mechanistic interpretations regarding the variable signal changes of other subunits across distinct experimental conditions; instead, our analysis focused on the robust, consistent loss of MED16 following 1,6-HD exposure.

      (2) Undermining the value of Figure 1E/F, UBP1 and TFCP2 may also associate with the small amount of MED16 in the 2MDa fractions. This is not tested, and therefore, the conclusion that they just associate with the dissociable form of MED16 is not supported.

      We thank the reviewer for this insightful observation. Indeed, UBP1 and TFCP2 can associate with MED16 in the context of the intact Mediator complex. Figure 1B directly supports this: under 2,5-HD conditions, where MED16 remains integrated into the Mediator, both UBP1 and TFCP2 are co-immunoprecipitated with the Mediator complex (via anti-MED1). This association is lost under 1,6-hexanediol conditions, where MED16 dissociates—suggesting that UBP1 and TFCP2 are tethered to the Mediator through MED16.

      The gel filtration experiment (Fig 1D) further reveals that a portion of MED16 resolves away from the core Mediator complex under these in vitro conditions, and Figure 1E-F shows that UBP1 and TFCP2 co-immunoprecipitated with MED16 in these lower molecular weight fractions. Taken together, these data indicate that UBP1 and TFCP2 can be found in association with MED16 in two biochemically distinguishable states: one integrated into the Mediator complex, and another that has dissociated from it under our experimental conditions. We did not state that UBP1 and TFCP2 associate exclusively with the dissociated form; rather, the dissociated fraction is where we initially identified this interaction, which then led us to investigate its functional significance. The whole manuscript has been extensively rewritten now, and hopefully the confusion has been clarified.

      Our functional data further demonstrate that the transcriptional activities of UBP1 and TFCP2 require the intact Mediator complex. MED16 bridges this interaction through its C-terminal αβ-domain, which binds UBP1/TFCP2, and its N-terminal WDR domain, which anchors MED16 into the Mediator. We have revised the manuscript to clearly distinguish the biochemical observation from the functional model, and we now explicitly state that UBP1 and TFCP2 associate with the Mediator complex through MED16.

      (3) Domain mapping studies in Figure 2 are overinterpreted. Since the interactions could be indirect, it is not accurate to conclude "Therefore, the N-terminal WDR domain of MED16 is crucial for its integration into the Mediator complex, while the C-terminal αβ-domain is essential for interacting with UBP1-TFCP2."

      We fully acknowledge that co-immunoprecipitation (co-IP) assays alone cannot discriminate direct physical binding from indirect protein associations mediated by intermediate partners. Nevertheless, our conclusion that MED16 directly interacts with UBP1 is supported by two orthogonal, independent experimental datasets.

      First, GST pull-down assays using purified recombinant proteins expressed in E. coli confirm direct binding (Figure 2C). This cell-free biochemical system removes all potential cellular bridging factors, unambiguously demonstrating a physical contact between the C-terminal αβ-domain of MED16 and UBP1.

      Second, our mutational functional analyses further rule out an indirect interaction model. Deletion of the unique 36-residue USP peptide in UBP1 completely abrogates MED16 binding (Figure 2F, comparison between ΔSAM and DBD truncations). Reciprocally, MED16 mutants lacking the C-terminal αβ-domain lose all capacity to associate with UBP1. Importantly, both truncation mutations yield identical functional defects in UBP1-driven and HIV-1 luciferase reporter assays (Figures 3G and 5G). If this complex assembly relied on an uncharacterized bridging protein, it would be statistically improbable for two distinct, structurally unrelated deletions (USP removal on the UBP1 side and αβ-domain truncation on the MED16 side) to independently disrupt the same indirect linkage and generate identical loss-of-function phenotypes.

      Collectively, the congruent results from cell-free in vitro biochemistry and targeted mutational functional assays provide robust evidence for a direct, specific physical interaction between MED16 and UBP1.

      (4) A close examination of Figure 2C undermines confidence in the association studies. The bait protein in lanes 5-8 should be equal. Also, there is significant binding of GST to UBP1 and TFCP2, in roughly the same patterns as they bind to GST-MED16 αβ. The absence of input samples makes the results even more difficult to interpret.

      We acknowledge the technical limitations the reviewer pointed out. Regarding the concern about unequal protein loading across lanes 5-8, we note that lanes 7 and 8 in fact contain more GST-M16αβ bait protein than lanes 5 and 6, yet TFCP2L1 and YY1 still show no appreciable binding above the GST-alone background. This loading asymmetry therefore strengthens the conclusion that these proteins do not directly interact with MED16. For UBP1 and TFCP2, we have now provided quantification of the Western blot signals, which confirms a clear difference in binding to GST-M16αβ versus GST alone. Furthermore, these GST pull-down experiments were performed under stringent conditions—300 mM NaCl and 0.5% Tween-20 in the wash buffer—and the UBP1 and TFCP2 interactions with GST-M16αβ persisted under these conditions (lane 5-8), whereas binding to GST alone was minimal (lane 1-4). These technical details support the specificity of the observed interactions.

      (5) The domain deletion mutants are utilized throughout the manuscript as evidence of the importance of the UBP1-MED16 interaction. However, in Figure 2F lanes 7 and 8, the delta-S mutant binds MED16 as well as full-length UBP1. This undermines much of the subsequent data and conclusions about specificity.

      We believe the reviewer may have a misunderstanding about the experimental design. The delta-S (ΔSAM) mutant deletes the SAM motif, which is required for UBP1-TFCP2 heterodimerization, but retains the UBP1-specific sequence (USP) that mediates MED16 binding. The observation that ΔSAM still binds MED16 at levels comparable to full-length UBP1 is therefore not a discrepancy—it is the expected result and confirms that USP is the region responsible for MED16 interaction. The DBD mutant, which further removes USP, completely loses MED16 binding. Together, these deletion constructs delineate two separable interaction surfaces on UBP1: USP for MED16 binding and the SAM motif for TFCP2 heterodimerization (see Figure 2 G). The functional analysis of these domains is reflected in Figure 3B: ΔSAM, which retains MED16 binding but loses TFCP2 dimerization, shows approximately half the transcriptional activity of full-length UBP1, suggesting that both interactions contribute to full UBP1 function. To avoid similar confusion, we have renamed ΔS to ΔSAM,UBP1-SP to USP in the revised manuscript.

      (6) Even if the delta-S mutant were defective for MED16 binding, the result in Figure 3B does not "confirm that MED16 is required for the transcriptional activity of UBP1,". Removal of that domain may have other effects.

      We wish to first clarify a factual point: the delta-S mutant (ΔSAM) is not defective for MED16 binding. ΔSAM deletes the SAM motif that is required for UBP1-TFCP2 heterodimerization, but retains the UBP1-specific sequence (USP) that mediates MED16 binding. As shown in Figure 2F, ΔSAM binds MED16 at levels comparable to full-length UBP1. The mutant that loses MED16 binding is DBD, which further deletes USP.

      The functional result in Figure 3B is therefore consistent with this biochemical mapping: ΔSAM retains MED16 binding but loses TFCP2 dimerization, and accordingly shows approximately half the transcriptional activity of full-length UBP1. This suggests that both the MED16 interaction (via USP) and the TFCP2 interaction (via the SAM motif) contribute to full UBP1 function.

      More importantly, the conclusion that MED16 is required for UBP1 transcriptional activity does not rest on domain mutant analysis alone. It is supported by independent loss-of-function approaches: shRNA-mediated MED16 knockdown (Fig 3C) and CRISPR-mediated MED16 knockout across five independent frameshift clones (Fig 3E-F), both of which consistently reduce UBP1 reporter activity. The domain deletion mutants complement these approaches by mapping the specific interaction interfaces.

      (7) As Mediator is critical for the activation of many genes, it is not accurate to assume that the impact of its deletion in Figure 3E/F demonstrates a direct requirement in UBP1-driven transcription. This could easily be an indirect effect.

      MED16 knockout data must be interpreted cautiously due to potential Mediator destabilization artifacts. However, our conclusion does not rely solely on loss-of-function data; transient competitive overexpression and domain-mapping assays bypass chronic MED16 depletion entirely. The MED16Δαβ mutant retains full Mediator integration but loses UBP1 binding. Unlike wild-type MED16, this mutant fails to change UBP1 or HIV-1 reporter activity in 36–48 h transient assays performed in wild-type cells with intact endogenous Mediator. Dose titration further confirms that only UBP1-interacting MED16 competes for UBP1 binding to repress target promoters (Fig 3G). Since these experiments involve no permanent MED16 ablation, the observed transcriptional effects cannot be attributed to indirect global Mediator dysfunction, verifying specific MED16–UBP1 regulation.

      (8) Without documenting the relative protein expression levels in Figure 3G/H, conclusions cannot be drawn about the titration experiments, nor the co-expression experiments. These findings are likely the result of squelching or some form of competition that is not directly related to the UBP1-mediated transcription. A great deal of validation would be required in order to support the model that these effects are a result of MED16 overexpression sequestering UBP1 away from holo-Mediator.

      We thank the reviewer for this insightful comment. In fact, the squelching interpretation that the reviewer proposes is fully consistent with our functional model and helps reinforce a key conclusion of our study.

      The reviewer correctly points out that overexpressed MED16 may compete with the endogenous Mediator complex for UBP1 binding, sequestering UBP1 away from the holo-Mediator and thereby inhibiting UBP1-driven transcription. This interpretation directly implies that UBP1 requires association with the intact Mediator complex—not merely free MED16—to activate transcription. This is precisely the model we advocate, and the squelching phenomenon observed in Figure 3G thus provides independent functional evidence for it.

      The specificity of this effect is confirmed by the MED16 Δαβ mutant: the Δαβ mutant integrates into the Mediator complex normally but cannot bind UBP1, and consequently fails to exert any competitive inhibition on the UBP1 reporter (Fig 3G). This demonstrates that the squelching effect is specifically dependent on the MED16-UBP1 interaction interface. We have now documented the relative protein expression levels for these experiments in the revised manuscript.

      (9) The lack of any documentation of expression levels for the various ectopic proteins in the majority of Figures, renders mechanistic claims meaningless (Figures 3, 4, 5, 6, 7, S2, S3). This is particularly relevant since the model presented for many of the results invokes concentration-dependent competition.

      In the revised manuscript, we have now included Western blot data documenting the expression levels of ectopic proteins for the key experiments. The expression level data confirm that the proteins were expressed at the expected levels across the different conditions.

      Recommendations for the authors:

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      Reviewer #1 (Recommendations for the authors):

      (1) The writing and presentation are generally good, except that some typos and mistakes in conceptual statements (e.g., lines 407-408 "WDR-deleted MED16, which cannot interact with UBP1") often confuse the readers.

      We thank the reviewer for the careful reading. We have tried out best to correct the typos and misstatements, including the error in line 407-408 where "WDR-deleted MED16, which cannot interact with UBP1" should refer to the αβ-domain deletion rather than the WDR deletion. The whole manuscript has been extensively rewritten for better clarity and preciseness.

      (2) Inconsistency in the citation format between numbering and author names should be avoided.

      The citation format has now been unified throughout the manuscript.

      (3) Two important scientific concepts based on the published literature should be mentioned:

      (a) "The position-dependent function of transcription factors (TFs) relative to the TSS" (lines 427-428) is a well-known fact in the transcription field that partly explains gene activation versus gene repression; and

      (b) the statement "JQ1 that is known to reverse HIV-1 latency by antagonizing BRD4's suppression of the Tat-P-TEFb/SEC (Super Elongation Complex) interaction" (lines 384-386) is only partially correct and is a property unique to the BRD4 long (BRD4-L) isoform.

      We thank the reviewer for this suggestion. We have now cited the relevant literature on the position-dependent function of transcription factors relative to the TSS, including the recent study by Duttke et al. (3), which systematically demonstrated that the effect of TF binding on transcription initiation is highly position-dependent. Our findings provide a clear example that UBP1 activates transcription when its binding motif is located upstream of the TSS but represses when the motif overlaps the TSS, which exemplifying this concept.

      We have corrected the statement to specify that JQ1 antagonizes the BRD4 long isoform (BRD4-L)-mediated suppression of Tat-P-TEFb/SEC, and have noted that the BRD4 short isoform (BRD4-S) may also contribute to HIV-1 promoter repression and JQ1-mediated latency reversal.

      (4) Another relevant concept is that BRD4 short (BRD4-S) isoform-inhibited HIV-1 promoter activity could be potentially alleviated by JQ1, leading to latency reversal. Besides the aforementioned points and those comments already provided in the Public Review, the involvement of TFCP2 and YY1 in MED16-UBP1 function has not been clearly established and will require more careful and detailed investigation.

      We thank the reviewer for these suggestions. Regarding BRD4-S, we have added a discussion of the BRD4 short isoform's role in HIV-1 promoter regulation and its potential contribution to JQ1-mediated latency reversal.

      Regarding the involvement of TFCP2 and YY1, we agree that their precise roles within the MED16-UBP1 regulatory axis have not been fully dissected in the current study. Our data indicate that TFCP2 can dimerize with UBP1 to enhance transcriptional activation (Fig 3H), and YY1 is present in the MED16-containing fractions (Fig 1E), consistent with its previously reported role in HIV-1 silencing. However, the molecular details of how TFCP2 and YY1 contribute to MED16-UBP1 function—and whether they act in the same or parallel pathways—remain to be determined. We have acknowledged this as a limitation and a direction for future investigation in the revised Discussion.

      Reviewer #2 (Recommendations for the authors):

      (1) The data from Figure 1F are hard to interpret, the blot quality is poor for Med23, 16

      We acknowledge that the blot quality for MED23 and MED16 in Figure 1F was suboptimal in the original submission. We have now provided better Western blot, which confirms the specific co-immunoprecipitation of MED16 and MED23 with TFCP2 in both HeLa nuclear extract and the gel filtration fractions. Importantly, the reciprocal co-IP (anti-MED16 IP blotted for TFCP2 and UBP1, Fig 1E) independently validates this interaction.

      (2) Figure 2: No MW markers on gels. Also, these types of experiments are ok for pilot studies, but they aren't rigorous. Only a few proteins were tested, and it's easy to get false positives with how the experiments were completed. Finally, the Mediator proteins were tested as single isolated proteins rather than in their native context. As isolated proteins, they likely have large exposed hydrophobic patches (normally bound to other subunits), and this can generate non-specific "sticky" interactions that can lead to false positive results.

      We have now included MW markers on the gel images in the revised Figure 2.

      The reviewer raises a valid general concern about domain mapping experiments. However, we wish to emphasize that the readout in Figure 2B and 2F is the binding of exogenously expressed truncated proteins to their endogenous interaction partners—MED1, TFCP2, MED16, and MED23—which are natively folded and assembled in their physiological environment. This is not a situation where two isolated proteins are mixed in a test tube. Furthermore, the deletion series themselves contain important internal controls against non-specific "stickiness": In Figure 2B, progressive deletion of the WDR domains leads to a gradual reduction in MED1 binding, consistent with the predicted structural role of the β-propeller (WDR domain). The truncation at the αβ-domain specifically abolishes TFCP2 binding but does not affect its association with Mediator complex as indicated by MED1. If the truncated proteins were non-specifically sticky due to exposed hydrophobic patches, one would expect uniform binding across all truncations or random binding patterns, which is not observed. And in Figure 2F, deletion of the 36-amino-acid USP abolishes its MED16 binding, while deletion of the SAM motif does not. This sequence-specific differential effect is inconsistent with non-specific hydrophobic interactions.

      The domain mapping experiments in Figure 2 were not designed as an unbiased screen but as hypothesis-driven tests guided by the IP-MS results identifying UBP1/TFCP2 as MED16 interactors, sequence and structural analysis predicting the roles of the WDR and αβ-domains. The conclusions from these experiments are corroborated by subsequent functional assays: the USP-deleted mutant and the MED16 Δαβ mutant show corresponding functional defects in both UBP1 reporter and HIV-1 reporter assays (Fig 3G, Fig 5G), providing strong support that the mapped interaction interfaces are functionally relevant.

      (3) Figure 3: UBP1 reporter is artificial, 6X UBP1 sites upstream of a TATA box.

      We appreciate this comment and wish to clarify a misunderstanding. The UBP1 reporter used in Figure 3 is not an artificial construct assembled from random elements. Its backbone is derived from the mouse α-globin gene promoter, a well-established natural target of UBP1/TFCP2, which contains two endogenous UBP1/TFCP2 binding sites. In order to increase the reporter's sensitivity to UBP1/TFCP2, we expanded the original two sites to six. Critically, this expansion did not alter the native architecture of the promoter—the relative positions of the transcription factor binding sites, the CCAAT box, the TATA box, and the TSS remain unchanged from the original α-globin promoter. This reporter therefore retains the physiological spacing and topological organization of a genuine UBP1 target promoter, and its low basal activity and high inducibility by UBP1 make it a well-suited tool for modeling UBP1 transcriptional activity. We have further clarified this description in the revised manuscript.

      (4) Figure 3D: Med16 KD not compelling, perhaps 50% knockdown? Hard to tell because no replicates and no quantitation.

      We acknowledge that the shRNA-mediated MED16 knockdown in Figure 3D was partial and that the original presentation lacked replicate quantification. We initially tested three independent shRNAs targeting MED16, of which only one achieved appreciable knockdown. Recognizing this limitation, we turned to a more definitive loss-of-function approach: CRISPR-Cas9-mediated MED16 knockout. Five independent frameshift KO clones were generated in mouse HT cells, each verified by sequencing and Western blotting (Fig 3F), and all five clones consistently showed reduced UBP1 reporter activity compared with wild-type cells (Fig 3E). The consistency between the shRNA knockdown and CRISPR KO reinforce the conclusion that MED16 is required for UBP1 transcriptional activity.

      (5) Figure 3E: Med16 KO will be a different cell line because of genetic adaptation. That is, an RNA-seq experiment is expected to result in many gene expression changes compared to the parental line. This could contribute to differences, or differences could result from altered Mediator composition to compensate for Med16 loss, but this wasn't addressed by the authors. A rapid degron method would be much more rigorous.

      We thank the reviewer for raising this important concern. We agree that prolonged MED16 loss can elicit cellular adaptation and compensatory changes in Mediator composition, as our own new data on tail module dissociation confirm. This makes it challenging to disentangle direct and indirect effects using constitutive KO models alone.

      However, two independent lines of evidence support a direct role for MED16 in UBP1-mediated transcription that cannot be explained by adaptive responses:

      First, acute shRNA-mediated knockdown produces the same phenotypic trend. shRNA operates on a shorter timescale (days) without prolonged selection for compensatory mutations, yet it also reduces UBP1 reporter activity (Fig 3C). The convergence of acute and chronic loss-of-function approaches suggests the phenotype is driven by MED16 loss itself rather than by the secondary adaptations that accumulate over time in KO cells.

      Second, and most critically, the MED16Δαβ mutant experiment completely bypasses the issues of genetic adaptation and Mediator structural disruption (Fig 3G). This experiment is performed in wild-type cells with an intact endogenous Mediator complex, without any MED16 depletion and without any long-term selection. The MED16Δαβ mutant retains full ability to integrate into the Mediator complex (its WDR domain is intact) but cannot bind UBP1. Overexpression of wild-type MED16 competitively inhibits the UBP1 reporter due to the squelching effect, whereas overexpression of MED16Δαβ at comparable levels does not (Fig 3G). The sole molecular difference is that MED16Δαβ doesn`t contain the UBP1-binding interface. Because this experiment involves no MED16 loss and no adaptive period, the result directly demonstrates that MED16-UBP1 interaction is required for UBP1 transcriptional activity, entirely independent of concerns about clonal adaptation.

      We appreciate the reviewer's suggestion of a rapid auxin-inducible degron approach. While acute depletion strategies offer clear advantages in minimizing cellular adaptation, they are not without limitations. First, degron-based depletion rarely achieves 100% elimination of the target protein, and residual protein may confound interpretation. Second, and particularly relevant to our system, degron-mediated degradation may preferentially target free, unassembled subunits while sparing proteins already stably integrated into large macromolecular complexes such as the Mediator—potentially creating a lag or even complete resistance to degradation for the pool of MED16 that is assembled into the complex. Given these considerations, we believe our convergent strategy—combining shRNA knockdown, CRISPR-mediated knockout in multiple independent clones, and the Δαβ mutant that bypasses structural perturbation—provides a robust and complementary set of evidence that does not rely on any single depletion method.

      (6) The title for Figure 3 is "Med16 is required for UBP1 activation of gene transcription", but that's not conclusively shown by these experiments.

      We agree that the original title of Figure 3 may overstate. We have revised the title to " MED16 acts through the Mediator complex to support UBP1-mediated transcriptional activation ", which may better reflect the data. As discussed in our responses above, this conclusion is supported by converging evidence from shRNA knockdown (Fig 3C), CRISPR-mediated KO across multiple clones (Fig 3E-F), and the specific loss of function observed with the MED16Δαβ mutant (Fig 3G).

      (7) Figure 5: Med16 protein levels were never measured, nor were other transfected proteins. Reporter assay data are correlative only; no evidence for direct effects, and only 2 other MED subunits are probed. Again, this is a starting point, but nothing to use for concrete conclusions about Med16.

      We have now provided Western blot data documenting MED16 and other transfected protein levels for the experiments in Figure 5, confirming that the proteins were expressed at the expected levels. We agree that plasmid-based reporter assays alone cannot establish direct transcriptional mechanisms. The HIV-1 reporter experiments in Figure 5 serve as an initial functional readout, and their conclusions are corroborated by more direct approaches: (i) the position-dependent specificity demonstrated by chimeric reporter assays (Fig 6A-C); (ii) the independence of the effect from TAR-mediated elongation and HDAC activity (Fig S2), ruling out alternative mechanisms; and (iii) the immobilized template transcription assays, which directly measure nascent RNA production and PIC assembly on the HIV-1 promoter (Fig 7). The reporter data should be viewed as one component of a larger body of convergent evidence rather than as standalone proof.

      MED23 and MED24 were chosen as the most relevant controls because they, along with MED16, form the core of the tail module submodule that creates the transcription factor interaction platform. They are structurally adjacent to MED16 within the same submodule. The fact that neither MED23 nor MED24 overexpression affects HIV-1 reporter activity (Fig 5C), whereas MED16 overexpression does so in a UBP1-binding-dependent manner (Fig 5C, 5E), demonstrates that the inhibitory effect is specific to MED16 and not a generic consequence of overexpressing a Mediator tail subunit.

      (8) Figure 6: similar concerns to Figure 5. The promoters are artificial, derived from a chicken beta-actin promoter with artificially inserted UBP1 binding sequences.

      We acknowledge that the chimeric promoters in Figure 6 are indeed deliberately designed. To address this specific question: Does the binding position of the UBP1 relative to the TSS dictate whether UBP1-MED16 activates or represses transcription?

      To isolate position as the sole variable, we needed a backbone promoter that (i) is stably and robustly active in the cell lines used, (ii) does not itself contain endogenous UBP1 binding sites that would confound the interpretation, and (iii) allows precise placement of the UBP1 binding motif either upstream of or overlapping the TSS. The chicken β-actin promoter meets all three criteria and is a widely used tool in molecular biology for such controlled promoter engineering experiments. Notably, its high GC content around the promoter region is also a feature shared with the HIV-1 core promoter, making it a particularly relevant backbone for the HIV-1 TSS chimeric constructs.

      The key controls for this experiment are internal: in the same backbone, placing the UBP1 binding site at the TSS results in MED16-UBP1-dependent repression, whereas placing it upstream does not (Fig 6A vs 6B). Furthermore, this position-dependent effect requires the MED16-UBP1 interaction interface, as the MED16 Δαβ mutant and the UBP1 DBD mutant both fail to produce the inhibitory effect (Fig 6A). The differential outcome between the two constructs—which differ only in the placement of the UBP1 binding site—cannot be attributed to the artificial nature of the backbone, because the backbone is identical in both cases.

      (9) Figure 6D: Not rigorous, no statistical analysis, cannot determine whether the modest changes are biologically relevant/meaningful. Also, data were obtained through indirect measurements and long timeframes, so any direct effects from possible UBP1 binding are probably lost.

      The differentially expressed genes in Figure 6D were selected using DESeq2 with a significance threshold of p < 0.05. The top 50 upregulated and top 50 downregulated transcripts were then used for UBP1 motif enrichment analysis around their promoters. We have clarified these selection criteria in the revised figure legend.

      Regarding the concern that indirect measurements over long timeframes, we now have additional evidence that UBP1 binding effects are preserved in our RNA-seq data. Motif analysis of UBP1-upregulated genes using HOMER identified the CP2 family binding motif (CNRG-N6-CNRG) as the most significantly enriched motif. This indicates that UBP1-binding-dependent transcriptional effects are not lost in our dataset.

      Author response image 4.

      Motif enrichment result of the UBP1 up-regulated genes

      Furthermore, when these UBP1-upregulated and UBP1-downregulated genes are analyzed separately for motif position relative to the TSS, a clear pattern emerges: UBP1-upregulated genes show motif enrichment flanking the TSS, whereas UBP1-downregulated genes show enrichment overlapping the TSS (Fig 6D). If the transcriptional changes were non-specific secondary consequences of prolonged UBP1 overexpression, one would not expect this position-specific distribution. Finally, this positional specificity is corroborated by the chimeric reporter experiments (Fig 6A-C), where moving the UBP1 binding site across the TSS in an otherwise identical promoter directly switches the regulatory outcome. The concordance among the de novo motif discovery, the genome-wide positional analysis, and the functional reporter assays supports the biological relevance of the motif enrichment pattern.

      (10) The title for Figure 6 is "UBP1 binding site determines the UBP1-Med16 inhibition in HIV-1 transcription" but the data do not convincingly show this.

      We appreciate this suggestion. We have revised the title to "The position of the UBP1 binding site relative to the TSS dictates whether MED16-UBP1 activates or represses transcription.". For clarification, Figure 6 not only demonstrates that the UBP1 binding position dictates MED16-UBP1-mediated inhibition in the HIV-1 context, but also revealed that this positional effect could be generalized to endogenous genes: UBP1 motifs flanking the TSS are associated with activation, while motifs overlapping with the TSS are associated with repression.

      (11) Figure 7: The data and the experiment do not support the claim in the title that "MED16-UBP1 complex prohibited PIC formation of HIV-1 transcription". I cannot convince myself that there are meaningful differences between the results shown in 7B, but it's hard to judge because there are no replicates and no quantitation.

      We have revised the title from "MED16-UBP1 complex prohibited PIC formation of HIV-1 transcription" to "MED16 interferes with HIV-1 PIC assembly at the TSS via its interaction with UBP1." We have also provided quantification of the Western blot signals from replicate experiments in Figure 7B, which confirms statistically significant differences in CDK8 binding, Pol II pSer5 signals, and TFIIB retention between conditions. We note that the key observations in Figure 7B were consistently observed across multiple pilot experiments prior to the final dataset presented in the manuscript. Thus, the reported differences are reproducible and not based on a single experiment.

      (12) The immobilized template experiments had only -78 upstream and +60 downstream of the TSS. By my understanding, much more upstream DNA is needed to allow a PIC to assemble, because the 5'-end bead attachment will block. I think this was outlined in an article by the Carey lab, but I'm not sure. A point here is that it would be reassuring for the authors to show that PIC assembly was occurring and that transcription was responsive to a transcription factor, similar to what the Carey lab has done with immobilized templates. See, for example, Figure 1 in Lin & Carey Curr Protocol Mol Biol 2012 Ch12 unit 12.14.

      We thank the reviewer for raising this technical consideration. We respectfully offer a different perspective based on current structural data on the human PIC.

      A recent cryo-EM study of the human Mediator-bound preinitiation complex (4) demonstrated that the PIC spans approximately 50 bp upstream and downstream of the TSS. The DNA templates used for structural determination in that study were generally within 105 bp upstream and 63 bp downstream of the TSS. Our HIV-1 template (-78 to +60, i.e., 78 bp upstream and 60 bp downstream of the TSS) falls within this well-characterized range and provides sufficient space for PIC assembly.

      Regarding the choice of the -78 boundary: the HIV-1 core promoter is defined as -78 to +60, and the region from -78 to -105 encompasses the HIV-1 enhancer, which contains additional regulatory elements including NF-κB and NFAT binding sites. We deliberately excluded these enhancer elements to avoid confounding regulatory inputs and to focus specifically on the UBP1 binding site at the TSS, which is the central focus of our model. The robust luciferase mRNA production observed in wild-type nuclear extract (Fig 7C) provides direct functional evidence that PIC assembly and transcription initiation proceed efficiently on this template under our conditions.

      Author response image 5.

      Promoter templates used for human PIC assembly (Chen et al., Science 372, eaba8490, 2021): templates within −105 to +65 bp of the TSS support PIC formation

      (13) Related to the above concern, the qPCR analysis from the extract experiments may be misleading because of contaminating nucleic acids in the extract. More control experiments are needed, such as no NTP controls.

      We thought this concern has been addressed by our experimental design.

      First, the PIC step in our experimental design (Fig 7B, "PIC") is the no-NTP control: nuclear extract is incubated with the immobilized template without NTPs. After washing, the proteins bound at this step are analyzed by Western blot, and importantly, there is no transcription occurred at this step. Transcription is then initiated by adding NTPs (Fig 7B, "Trx."), and the supernatant from this step is collected for qPCR analysis of transcription.

      Second, it is unlikely that the contaminating nucleic acids from the nuclear extract could confound our qPCR results, because the qPCR analysis specifically detects Luciferase mRNA transcribed from the immobilized HIV-1-Luciferase template, which is absent from eukaryotic genomes; and the endogenous genomic DNA or RNA cannot generate a false-positive signal.

      Lastly, after transcription, the immobilized templates are retained on the streptavidin beads and removed, while only the supernatant containing newly synthesized RNA is collected for qPCR. Any residual template DNA or extract-derived nucleic acids bound to the beads are therefore excluded from the qPCR sample.

      (14) Some references are made with the format of (author name, year), but others use numbers.

      The citation format has been unified throughout the manuscript.

      (15) On line 111-115, it is stated that 2,5 HD somehow "does not interfere with weak hydrophobic interaction due to different positions of hydroxyls" with no reference. Both 1,6 HD and 2,5 HD are hydrophobic molecules, and they will disrupt protein structure. The authors make a point about the differential results with 1,6 HD vs. 2,5 HD, but it's not compelling and not justified.

      We have now provided the reference to support this statement. The specificity of the 1,6-HD effect is further clarified as follows.

      1,6-HD and 2,5-HD are structural isomers sharing the same chemical formula and molecular weight. The key difference lies in the positions of their hydroxyl groups (See Author response image 6). The position of the two hydroxyl groups changes both the molecule’s shape and how it interacts with water and hydrophobic surfaces.

      In 1,6-HD, the two -OH groups sit at the ends of the chain, giving a more symmetric, flexible molecule, while in 2,5-hexanediol the -OH groups are closer to the middle, which changes polarity distribution and local geometry (5, 6). The distinctive structures matter because hydroxyl groups strongly hydrogen-bond with water. When they are terminal and well separated in 1,6-HD, the molecule can present a more balanced hydrophilic “cap” at both ends while keeping a relatively long hydrophobic center, which helps 1,6-HD partition into and disturb weakly interacting hydrophobic regions in condensates or hydrophobic interfaces (Author response image 6). By contrast, moving the hydroxyls inward in 2,5-HD does not help with disrupting the hydrophobic interfaces (Author response image 6). That is why 2,5-HD is often used as a comparison compound: it has the similar chemical formula, but the different hydroxyl pattern usually makes it a weaker or at least differently acting perturbant.

      Author response image 6.

      Differential disruption of hydrophobic interfaces by 1,6-HD vs 2,5-HD

      (16) On lines 117-120, the authors claim that Med16 is "integrated into the Mediator complex through weak hydrophobic interaction but not ionic interaction" based upon a few IP results. They then state (lines 130-131) that "the interaction between Mediator and Med16 is an unstable hydrophobic interaction." These statements make no sense based on any data shown, and they ignore the fact that Med16 purifies with Mediator from many different purification protocols. It's safe to say that its interaction with Mediator results from strong, extensive hydrophobic interfaces, which is further supported by actual structural data. The authors should check their reasoning here because they are over-interpreting their results, and this will be misleading to readers.

      We thank the reviewer for this important suggestion. The reviewer is right that our original phrasing—particularly "unstable hydrophobic interaction"—was imprecise and inconsistent with the established structural data. As the reviewer notes, MED16 co-purifies with the Mediator complex through multiple purification protocols, and recent cryo-EM structures (4) reveal that MED16 engages in extensive hydrophobic interfaces with neighboring tail module subunits, particularly MED24 and MED25. We fully agree that this is a strong, hydrophobic interaction in the physiological context.

      Our 1,6-hexanediol experiments demonstrated that the MED16-Mediator interface does have a hydrophobic character, which renders the sensitivity to 1,6-HD disruption. This does not mean the interaction is weak. We have now carefully described the relevant statements in the revised manuscript.

      Reviewer #3 (Recommendations for the authors):

      (1) It is not accurate to claim that "198 (genes) were repressed by shMED16 knockdown (Fig 4B and 4C)". These genes were simply not activated by UBP1 in the knockdown cells.

      We thank the reviewer for this careful distinction. We have revised the text to state that the UBP1-activated genes were “profoundly downregulated upon MED16 depletion” rather than “repressed by shMED16 knockdown”. The biological point remains unchanged: MED16 is required for UBP1 to activate this set of target genes.

      (2) Like the majority of experiments presented, the RNA-seq presented in Figure 4 may reflect indirect effects of MED16 loss. As such, the results do not support the claim that "This result demonstrated that UBP1 and MED16 collaborate to regulate transcription".

      We acknowledge that RNA-seq data from MED16 knockdown cells alone cannot distinguish direct from indirect effects. However, we did not rely on the RNA-seq data alone to support a collaborative role for UBP1 and MED16.

      The key evidence comes from experiments comparing wild-type MED16 with the MED16Δαβ mutant. MED16Δαβ integrates into the Mediator complex normally but cannot bind UBP1 (Fig 2B). When overexpressed in wild-type cells, wild-type MED16 inhibits the UBP1-driven reporter activity through a squelching effect. In contrast, MED16Δαβ has no effect on the UBP1 reporter assay (Fig 3G), and the same is true for the HIV-1 reporter assays (Fig 5G). Thus, MED16 must both integrate into the Mediator complex and directly bind UBP1 to support UBP1-mediated transcription.

      In addition, the genes identified as UBP1-MED16 co-regulated by RNA-seq are enriched for specific biological processes—angiogenesis (Fig 4D-E)—that align with the known physiological functions of UBP1. Such functional coherence would be unexpected if the transcriptional changes were merely non-specific secondary consequences of Mediator disruption.

      Thus, while we agree that RNA-seq alone has limitations, the convergent evidence from the MED16Δαβ mutant and the functional specificity of the co-regulated gene set supports a collaborative role for UBP1 and MED16 in transcriptional regulation.

      (3) The data in Figure 4 are not presented with controls documenting the relative expression levels of MED16 and UBP1. For example, it would be important to understand the level of UBP1 overexpression achieved, relative to endogenous UBP1 levels in HeLa cells.

      We agree with the reviewer that documenting expression levels is important. Author response image 7 are the CPM values for UBP1 from our RNA-seq data, confirming robust overexpression in the UBP1-overexpression condition compared with the control. The MED16 protein levels in the knockdown cells can be found in Fig 3D, as the same MED16-knockdown HeLa cell line was used for the RNA-seq experiment.

      Author response image 7.

      UBP1 expression level derived from RNA-seq

      Regarding the physiological relevance of UBP1 overexpression, several observations support that the transcriptional effects we observe are specific rather than artifacts of overexpression: (i) the UBP1-activated genes are enriched for pathways known to be regulated by UBP1—angiogenesis (Fig 4D-E)—rather than showing random, non-specific activation; (ii) the activation of these genes is attenuated upon MED16 knockdown, demonstrating dependence on the MED16-UBP1 interaction; and (iii) in reporter assays, the MED16 mutant lacking its C-terminal αβ-domain fails to competitively inhibit UBP1 driven reporter transcription even when UBP1 is similarly overexpressed (Fig 3G), indicating that the competitive inhibition of the UBP1 reporter depends specifically on the MED16-UBP1 interaction interface, rather than on the amount of UBP1 protein overexpressed.

      (4) The enhancer-promoter architecture of the luciferase construct used in Figure 3 is not sufficiently described. Are the elements far enough apart that Mediator-dependent looping is required for transcription, for example?

      We appreciate the reviewer's question. The UBP1 reporter construct used in Figure 3 is derived from the mouse α-globin gene promoter, a well-established natural target of UBP1/TFCP2(7, 8). The native α-globin promoter contains two endogenous UBP1/TFCP2 binding sites; to increase the reporter's sensitivity to UBP1, we expanded these to six tandem copies. Critically, this expansion preserved the native architecture of the promoter—the relative positions of the UBP1 binding sites, the CCAAT box, the TATA box, and the TSS were not altered. The entire reporter construct spans only 198 bp in total length. At this scale, all regulatory elements reside within a compact proximal promoter region, with no element separated by a distance that would require DNA looping for functional communication. The reporter therefore measures UBP1-dependent transcriptional activation from the proximal promoter, consistent with the canonical role of Mediator in facilitating PIC assembly rather than mediating long-range enhancer-promoter interactions. We have added a detailed description of the construct architecture to the Methods section.

      (5) The identification of matches to the UBP1 consensus sequence in a promoter does not prove that this is how UBP1 overexpression is impacting their transcription. "highly matched UBP1 motifs were identified in their promoters (Fig. 4F), leading to their activation when UBP1 was overexpressed."

      We agree with the reviewer that the presence of a UBP1 consensus motif in a promoter does not by itself prove direct regulation. We have revised the original statement: the activation of these genes by UBP1 overexpression is now described as "associated with" the presence of UBP1 motifs in their promoters.

      We acknowledge that the ideal experiment to demonstrate direct UBP1 occupancy would be ChIP-seq. However, ChIP-grade antibodies against UBP1 are not commercially available, and no UBP1 ChIP-seq datasets exist in public databases. We attempted to carry out the ChIP-seq ourselves but were unable to obtain sufficiently good-quality data.

      In the absence of ChIP data, three lines of evidence indicate that the identified UBP1 motifs are functionally relevant. First, motif analysis of UBP1-upregulated genes using HOMER identified the CP2 family binding motif (CNRG-N6-CNRG) as the most significantly enriched motif. This demonstrates that UBP1-binding-dependent transcriptional could be preserved in our RNA-seq data. Second, when we examine the positional distribution of these motifs, a clear pattern emerges: UBP1 motifs flank the TSS in activated genes, but overlap the TSS in repressed genes (Fig 6D). Indirect effects would not produce this position-specific pattern. Third, our chimeric reporter experiments (Fig 6A-C) directly test this position effect: moving the UBP1 binding site upstream of the TSS leads to activation, while moving it to overlap the TSS leads to repression. Together, these three independent approaches support the conclusion that UBP1 regulates these genes through the motifs we identified. See Author response image 4.

      (6) Lines 266 and 277 are redundant.

      We have carefully reviewed lines 266 and 277. Line 266 is the topic sentence (caption) of the section ("MED16 collaborates with UBP1 to inhibit HIV-1 transcription"), which introduces the overall conclusion, while line 277 describes the specific experimental result ("MED16 inhibited HIV-luciferase activity in a dose-dependent manner"). These serve distinct rhetorical functions. We have ensured that no other redundant phrasing exists elsewhere in the section.

      (7) The mere correlation of binding site position with activation status does not prove "that UBP1 may act as either a transcriptional activator or repressor, depending on distance of its binding site to TSS."

      We appreciate the reviewer's concern regarding the distinction between correlation and causation. The genome-wide analysis (Fig 6D) reveals an association between UBP1 motif position and transcriptional outcome, which prompts us to postulate that the UBP1 binding position could dictate the transcriptional activity level. The causal evidence comes from the chimeric reporter experiments (Fig 6A-C), in which we directly manipulated the position of the UBP1 binding site relative to the TSS in an otherwise identical promoter background. Placing the UBP1 binding site at the TSS resulted in MED16-UBP1-dependent repression, whereas placing it upstream of the TSS did not. This controlled experiment demonstrates that changing only the position of the UBP1 binding site is sufficient to switch the regulatory outcome, thereby establishing a causal relationship between binding site position and transcriptional effect. We have revised the text to clearly distinguish the correlational genome-wide analysis from the causal reporter experiments.

      (8) In Figure 7, MED16 overexpression results in less CDK8 binding. This undermines the claim that MED16 overexpression lowers transcription by binding to UBP1, and suggests an indirect effect.

      We appreciate the reviewer's careful observation. The reduction in CDK8 binding upon MED16 overexpression is consistent with our model: MED16, through UBP1 occupying the TSS, interferes with PIC assembly, of which CDK8 is a component. Importantly, the input levels of CDK8 and other Mediator subunits remained unchanged across the three conditions (Fig 7B), indicating that the reduced CDK8 signal in the PIC is not due to altered protein expression.

      In support of the specificity of the transcriptional effect, MED16Δαβ—which integrates into the Mediator complex but cannot bind UBP1 (Fig 2B)—fails to competitively inhibit either the UBP1 reporter (Fig 3G) or the HIV-1 reporter (Fig 5G), confirming that the transcriptional outcome depends on the MED16-UBP1 interaction.

      (9) Figure 8F is not referenced in the text or legend.

      Figure 8F has now been referenced in the Results and Discussion section.

      Reference:

      (1) Wang G, Cantin GT, Stevens JL, Berk AJ. Characterization of mediator complexes from HeLa cell nuclear extract. Mol Cell Biol. 2001;21(14):4604-13.

      (2) Yamazaki T, Souquere S, Chujo T, Kobelke S, Chong YS, Fox AH, et al. Functional Domains of NEAT1 Architectural lncRNA Induce Paraspeckle Assembly through Phase Separation. Molecular Cell. 2018;70(6):1038-+.

      (3) Duttke SH, Guzman C, Chang M, Delos Santos NP, McDonald BR, Xie J, et al. Position-dependent function of human sequence-specific transcription factors. Nature. 2024;631(8022):891-8.

      (4) Chen X, Yin X, Li J, Wu Z, Qi Y, Wang X, et al. Structures of the human Mediator and Mediator-bound preinitiation complex. Science. 2021;372(6546).

      (5) Zheng T, Wake N, Weng SL, Perdikari TM, Murthy AC, Mittal J, et al. Molecular insights into the effect of 1,6-hexanediol on FUS phase separation. EMBO J. 2025;44(10):2725-40.

      (6) Romero CM, Páez MS, Miranda JA, Hernández DJ, Oviedo LE. Effect of temperature on the surface tension of diluted aqueous solutions of 1,2-hexanediol, 1,5-hexanediol, 1,6-hexanediol and 2,5-hexanediol. Fluid Phase Equilibr. 2007;258(1):67-72.

      (7) Lim LC, Swendeman SL, Sheffery M. Molecular cloning of the alpha-globin transcription factor CP2. Mol Cell Biol. 1992;12(2):828-35.

      (8) Chae JH, Kim CG. CP2 binding to the promoter is essential for the enhanced transcription of globin genes in erythroid cells. Mol Cells. 2003;15(1):40-7.

    1. eLife Assessment

      This is a useful study that seeks to elucidate the molecular mechanisms underlying spinal circuit assembly. The authors demonstrate that loss of Onecut transcription factors in spinal motor neurons affects the size and spatial distribution of pre-motor interneurons. The main conclusion that Onecut acts through Neurotrophin-3 to regulate interneuron development in a non-cell autonomous manner is partially supported by the data, rendering the study incomplete. The work will be of broad interest to cell and developmental biologists.

    2. Reviewer #1 (Public review):

      Summary:

      This is a useful study by Angla et al that investigates the basis of observations made from previous studies where loss of Onecut (OC) transcription factors lead to changes in spinal interneuron populations that do not themselves normally express OC. The authors hypothesize that OC expression in spinal motor neurons has non-cell-autonomous effects on pre-motor interneuron (V1, V2a/b/c) population size and distribution. By knocking out OC in the motor neuron lineage (i.e., downstream of Olig2, a motor neuron progenitor marker gene), they indeed show that motor neuron-specific loss of OC expression decreases V2c interneuron number and alters the spatial distribution of V1, V2a, V2b, V2c populations. Using bulk RNA-sequencing of WT and OC conditional knockout (cKO) motor neurons, the authors identify that neurotrophic factor Ntf3 as another factor that is responsible for the non-cell-autonomous effects on the interneuron population; upon knock out of Ntf3 downstream of Olig2 they show that this leads to increased interneuron numbers and alters their spatial distribution, ultimately leading to dysregulation of spinal motor circuits and motor activity.

      Strengths:

      The authors use sophisticated genetic tools to precisely remove OC and Ntf3 expression in a lineage-specific manner and comprehensively assess the downstream effects across brachial, thoracic, lumbar levels of the spinal cord, as well as at two developmental timepoints, E12.5 and E14.5.

      Weaknesses:

      The evidence supporting their claims is incomplete. In figure 2 and 4, the control distributions (despite reporting the same populations in the same regions) look different, suggesting large sample-to-sample variance in distribution. The authors explain that this is due to several technical limitations in their study.

      Furthermore, the way the Ntf3 results are framed can be misleading; it reads as though one of the main claims of this study is that OC acts through Ntf3.

    3. Reviewer #2 (Public review):

      The study by Angla et al. proposes a model in which NT-3 produced by motor neurons regulates interneuron numbers and distribution in a non-cell autonomous manner. The authors demonstrate that ablation of motor neurons (MNs) and global and conditional deletion of OC transcription factors lead to changes in interneuron distribution. They identify that NT3 is upregulated after MN-specific OC deletion in RNA-seq experiments and show that olig2-cre mediated NT3 deletion leads to increased ventral interneuron numbers, altered distribution and defects in locomotor behavior. The authors conclude that MN-derived NT3, under OC control, regulates interneuron development. While this is an intriguing hypothesis, the study does not consolidate the results from the different experimental models in a cohesive model to provide any mechanistic insights into how NT3 may accomplish this function.

      (1) The authors utilize different experimental models (OC transcription factor global and conditional knockouts, DTA ablation and NT3 deletion) to show that MNs may influence interneuron numbers and positioning, but there doesn't seem to be a direct link into how each manipulation relates to the others in an informative way. For example, the relationship between OC and NT3 deletion data is not entirely clear, even though NT3 is downstream of OC transcription factors. Both deletions lead to changes in interneuron distribution but there doesn't seem to be any correlation between the two that relates to relative changes in NT3 levels. Although it is understandable that transcription factor manipulations may have additional effects, it is hard to derive any mechanistic insights into how exactly MNs influence interneuron differentiation or migration from the data presented here. Similarly, while the rationale for following up on NT3 seems to be the chick electroporation experiments, only a very minor decrease in Chx10 interneurons is shown in those experiments. Shouldn't NT3 overexpression lead to substantial decreases in IN numbers according to the authors' model?

      (2) The study primarily quantifies interneuron numbers and distribution at different levels of the spinal cord and under different genetic manipulations. In different figures, the values and distributions shown for controls vary quite a lot. For example, in figure 2B vs figure 4B, the number of FoxP2+ V1 neurons at brachial levels is ~350 vs 125. Similarly, the control distributions in 2I and 4I are quite different. While this can be partially explained by technical issues, it remains challenging to determine whether the conclusions regarding the impact of each genetic manipulation on interneuron numbers and distribution are valid.

      (3) It is not clear that the behavioral phenotypes seen in the olig2-cre mediated deletion of NT3 can be attributed to specific changes in interneuron development, connectivity or function. While the behavioral phenotype is in theory consistent with defects in motor circuits, there is a big gap between the embryonic changes shown here and behavior, with no in between circuit level changes in locomotor circuits shown. Thus, the behavioral experiments are not very informative as to how exactly components of motor circuits might be altered with the NT3 deletion.

    4. Reviewer #3 (Public review):

      This manuscript aims to investigate cell extrinsic mechanisms that regulate differentiation and distribution of interneuron types in the spinal cord. The authors demonstrate that the loss of motor neurons leads to changes in number and distribution of different interneuron types, specifically V0v, V1, and V2b (but not V2a). The authors then hypothesize that this phenotype may be controlled by the action of Onecut (OC) transcription factors in motor neurons. Conditional knockout of OC1 + OC2 in motor neurons using Olig2-Cre, however, does not lead to significant changes in numbers of V1, V2a, V2b interneurons, although there is a change in their spatial distribution. While the authors do not check V0v neurons in OC mutants, they do check V2c, which show a reduction in number and change in distribution. Why the same neurons are not checked across experiments is unclear. The authors then analyze existing RNA-seq data to identify factors that could be mediating effects of the OC factors in motor neurons. They identify Ntf3 as a candidate and confirm that it is upregulated in OC mutants. Conditional loss of function of Ntf3 (Olig2-Cre) leads to increases in V1, V2a, and V2b (but not V2c) interneurons and changes in distribution of all four interneuron types. However, the Ntf3 phenotypes do not necessarily suggest that it works in the same pathway as OC to control interneuron development. Finally, the authors demonstrate that these Ntf3 conditional mutants have major defects in motor function.

      The main conclusions of the manuscript are that i) motor neurons non-cell autonomously control interneuron development, ii) that OC activity in Olig2 lineage cells non-cell autonomously controls aspects of interneuron development, and iii) that Ntf3 in motor neurons non-cell autonomously controls aspects of interneuron development. The mechanistic link between OC and Ntf3 activity is not clear. And the interneuron phenotype seen when motor neurons are depleted it not phenocopied by either loss of OC or Ntf3 from motor neurons. While the authors addressed some of my initial comments, the impact of this work on the field remains difficult to assess for the following reasons:

      (1) The manuscript relies heavily on quantifying numbers and spatial distribution of interneuron populations. However, these do not seem to be consistent in control animals across experiments, making it difficult to interpret any changes observed in genetic manipulations. Specifically, in Fig. 2 and 4, the same markers are being used to quantify V1, V2a, V2b, and V2c interneurons in controls vs. OC (Fig. 2) or Ntf3 (Fig. 4) conditional knockouts, but the numbers of neurons and their distribution in control animals are variable between these two figures. For example, there seem to be a mean of >300 V1 neurons in E12.5 brachial sections of Fig. 2 controls, but this number is <150 in Fig. 4 controls. The cell distribution scoring is similarly variable between these controls without any explanation. The same is true for E14.5 controls used in Fig. S2 vs. Fig. S6.<br /> The authors explain in response to the 1st review that technical variability including different antibodies, different researchers performing experiments, and different mouse backgrounds may contribute to this variation, but do not include this explanation clearly in the manuscript where it would be useful for readers. It would have also helped, for example, if authors had labeled datapoints on bar plots based on the animal that they came from to understand inter-animal variability within experiments. Overall, the biological significance of the results remain difficult to interpret due to high technical variability.

      (2) In the initial review, it was noted that there is no data to support the claim that OC1/2 mutants have altered interneuron development because of higher Ntf3 expression. In response to this, the authors note that the manuscript does not claim that OC exerts its non-cell autonomous effects through Ntf3 and add a new paragraph in the discussion which now addresses the fact that it is difficult to compare the phenotypes observed the different genetic manipulations presented in the work. While this paragraph addresses the incomplete mechanistic link between OC and Ntf3, it also decreases the mechanistic impact of the work.

      (3) In Figure 3E-3G, the authors show that some V1, V2a, V2b interneurons express the Ntf3 receptor TrkC. It is not stated (apologies if I missed it) what age this expression is, and from the images, it looks like in addition to medially located newborn neurons being negative, laterally located Gata3 interneurons are also broadly negative for TrkC, and many Foxd3 and Chx10 interneurons are also negative. Separate images of each channel and/or scoring would help clarify this. If many relevant interneurons are indeed TrkC negative, how this affects the conclusions should be addressed in the manuscript.

      (4) Behavioral phenotypes are not necessarily caused by Ntf3's role in premotor interneuron development.

    5. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In this study, Angla et al investigate the basis of observations made from previous studies where loss of Onecut (OC) transcription factors leads to changes in spinal interneuron populations that do not themselves normally express OC. The authors hypothesize that OC expression in spinal motor neurons has non-cell-autonomous effects on pre-motor interneuron (V1, V2a/b/c) population size and distribution. By knocking out OC in the motor neuron lineage (i.e., downstream of Olig2, a motor neuron progenitor marker gene), they indeed show that motor neuron-specific loss of OC expression decreases V2c interneuron number and alters the spatial distribution of V1, V2a, V2b, and V2c populations. Using bulk RNA-sequencing of WT and OC conditional knockout (cKO) motor neurons, the authors identify that the neurotrophic factor Ntf3 is downregulated by OC expression. They subsequently hypothesize that the non-cell-autonomous effects observed by loss of OC expression in motor neurons can be explained by de-repression of Ntf3. To test this, the authors conditionally knock out Ntf3 downstream of Olig2 and show that this leads to increased interneuron numbers and alters their spatial distribution, ultimately leading to dysregulation of spinal motor circuits and motor activity.

      Strengths:

      The authors use sophisticated genetic tools to precisely remove OC and Ntf3 expression in a lineage-specific manner and comprehensively assess the downstream effects across brachial, thoracic, lumbar levels of the spinal cord, as well as at two developmental timepoints, E12.5 and E14.5.

      Weaknesses:

      There are two main concerns that are not fully addressed:

      (1) Based on the effects observed with OC vs. Ntf3 cKO, it is unclear whether OC is indeed exerting its non-cell-autonomous effects via Ntf3. Knocking out both Ntf3 and OC and comparing the effects to those seen with just OC cKO alone could provide more insight on this point.

      In this study, we did not intend to demonstrate that Onecut transcription factors exert their non-cell autonomous action on spinal interneuron development by regulating Ntf3 expression, and we do not state in the manuscript that this is the case. We only show that Onecut factors and Ntf3, the expression of which they regulate, contribute to the non-cell autonomous regulation of spinal interneuron development by the motor neurons. We are convinced that Onecut factors could regulate multiple independent factors and pathways involved in extrinsic regulation of interneuron development. This possibly also includes, as demonstrated in cell culture for multiple homeoproteins including human Onecut factors, the intercellular transfer of the Onecut homeoproteins during spinal cord development, a process that we are currently investigating. Knocking out both OC and Ntf3 in the motor neurons, beyond being technically extremely challenging (1/64 probability to obtain double-mutant embryos), would not enable to address this question, as it will simply results in the addition of two different defects.

      Also, a quantitative summary of the effects of Ntf3 overexpression in motor neurons in the chick is lacking.

      A quantitative summary of the effects of Ntf3 overexpression in the chicken embryonic spinal cord is provided in Figure S2.

      (2) How the authors assess changes in the spatial distribution of interneurons is unclear. In Figures 2 and 4, the control distributions (despite reporting the same populations in the same regions) look different, suggesting large sample-to-sample variance in distribution. Although the authors report that several sections in each level were taken from at least three animals for each condition, it's unclear how variance within WT or cKO sections was accounted for in the final statistical evaluation. It seems at a glance that a comparison between control samples in Figure 2 and Figure 4 could report statistically significant differences, which would be problematic. A more rigorous report of sample-to-sample variance and a more in-depth explanation of the statistical methods are needed.

      The experimental procedure to analyze the spatial distribution of spinal interneurons at different stages of development is described in details in the “Statistical analyses” paragraph of the Materials and Methods section of the manuscript, and has been repeatedly used by ourselves [1,2] and by others (see for example [3-5]) to conduct similar analyses.

      We also noticed that the distribution of the different analyzed interneuron populations in the control embryos showed some differences between the cOc1Oc2<sup>-/-</sup> and in the cNtf3<sup>-/-</sup> lines. Several parameters can account for this observation. First, this study has been conducted over a period of 15 years, different investigators each contributing to different steps of the analysis. Second, the genetic background of these two lines is not identical, impacting both the duration of the gestation (hence, the embryonic stage of the performed analyses, even if the embryos were collected on the same gestation day) and possibly the distribution of some interneuron populations. Third, because of evolutions in the availability of the primary antibodies used to label the interneuron populations of interest, the same antibodies were not used throughout the study, as stated for in the Materials and Methods section for Chx10, Gata3 and Sox1, although the same antibody was used by the same investigator to label the same interneuron population in each mouse line at each developmental stage.

      A detailed description of the number of sections and embryos included in each analysis as well as the whole statistical workflow that was used for the distribution analyses, which takes into account variance within control or mutant samples, is now provided in the revised version of the manuscript.

      Reviewer #2 (Public review):

      The study by Angla et al. proposes a model in which NT-3 produced by motor neurons regulates interneuron numbers and distribution in a non-cell autonomous manner. The authors demonstrate that ablation of motor neurons (MNs) and global and conditional deletion of OC transcription factors lead to changes in interneuron distribution. They identify that NT3 is upregulated after MN-specific OC deletion in RNA-seq experiments and show that olig2-cre mediated NT3 deletion leads to increased ventral interneuron numbers, altered distribution, and defects in locomotor behavior. The authors conclude that MN-derived NT3, under OC control, regulates interneuron development. While this is an intriguing hypothesis, additional experiments are needed to support it and strengthen the link between the different experiments described here.

      (1) The study primarily quantifies interneuron numbers and distribution at different levels of the spinal cord and under different genetic manipulations. Experimental details are lacking, defining how many sections were analyzed (several are noted in the methods) and how the rostrocaudal levels of the spinal cord were precisely aligned.

      A detailed description of the number of sections and embryos included in each analysis as well as the whole statistical workflow that was used for the distribution analyses is now provided in the revised version of the manuscript. The rostrocaudal levels of the spinal cord were precisely aligned using the distribution of Foxp1 in the Lateral Motor Columns (LMCs) at brachial or lumbar levels of the spinal cord, which is also indicated in the revised version.

      In different figures, the values and distributions shown for controls vary quite a lot. For example, in Figure 2B vs Figure 4B, the number of FoxP2+ V1 neurons at brachial levels is ~350 vs 125. Similarly, the control distributions in 2I and 4I are quite different. This makes it challenging to determine whether the conclusions regarding the impact of each genetic manipulation on interneuron numbers and distribution are valid.

      Several parameters can account for these observations. First, this study has been conducted over a period of 15 years, different investigators each contributing to different steps of the analysis. Second, the genetic background of these two lines is not identical, impacting both the duration of the gestation (hence, the embryonic stage of the performed analyses, even if the embryos were collected on the same gestation day) and possibly the distribution of some interneuron populations. Third, because of evolutions in the availability of the primary antibodies used to label the interneuron populations of interest, the same antibodies were not used throughout the study, as stated for in the Materials and Methods section for Chx10, Gata3 and Sox1, although the same antibody was used by the same investigator to label the same interneuron population in each mouse line at each developmental stage.

      (2) The relationship between OC and NT3 deletion data is not entirely clear. Both deletions presumably lead to changes in interneuron distribution, but is there any reverse relationship between the two that relates to relative changes in NT3 levels? The authors do not directly compare NT3 and OC KO IN distributions. Similarly, one might expect a decrease in interneuron numbers in OC mutants, which is only reported for V2c neurons. However, the image presented in Figure 2G shows an equal number of V2c INs in control and mutant.

      In this study, we did not intend to demonstrate that Onecut transcription factors exert their non-cell autonomous action on spinal interneuron development by regulating Ntf3 expression, and we do not state in the manuscript that this is the case. We only show that Onecut factors and Ntf3, the expression of which they regulate, contribute to the non-cell autonomous regulation of spinal interneuron development by the motor neurons. We are convinced that Onecut factors could regulate multiple independent factors and pathways involved in extrinsic regulation of interneuron development. For example, as mentioned above, we observed intercellular transfer of the Onecut homeoproteins during spinal cord development, suggesting different strategies for non-cell autonomous regulation of interneuron development. The two mouse lines studied here consist on the one side in a combination of OC inactivation and Ntf3 increased expression, and on the other side in Ntf3 inactivation. Therefore, a reverse relationship between the changes in interneuron distribution is not expected. Furthermore, gain-of-function and loss-of-function experiments in mouse models frequently generate phenotypes that are not inverse to each other.

      (3) It is not clear that the behavioral phenotypes seen in the olig2-cre mediated deletion of NT3 can be attributed to changes in interneuron development. How about a role of NT3 in oligodendrocytes? There is a big gap between the embryonic changes shown here and behavior, with no in-between circuit-level changes in locomotor circuits shown.

      We agree, the motor behavior changes that we recorded in Ntf3 conditional mutant mice are, as stated, “consistent with the hypothesis that Ntf3 produced by MNs is required to generate locomotor circuits with properly coordinated activity” but do not demonstrate a direct causal relationship. However, investigating the intrinsic activity of the spinal locomotor circuits, independently from, for example, oligodendrocyte contribution may prove to be extremely challenging and was beyond the scope of this study. In addition, to our best knowledge, Ntf3 has not been shown to be expressed in healthy oligodendrocytes in vivo, and TrkC has not been reported to be displayed by these cells in the same conditions.

      A more restricted manipulation would be deleting TrkC from specific interneuron populations. Related to this, although TrkC is shown to be broadly expressed in ventral interneurons, it is not shown specifically to colocalize with any of the interneuron markers. The authors should validate that the receptor is expressed in the subsets that they are investigating.

      We agree, investigating the consequences of inactivating the TrkC receptor in specific interneuron populations would be extremely informative. However, this experiment is also very challenging to perform, as most of the driver lines available to target spinal interneuron populations additionally target multiple neuronal populations outside of the spinal cord that are also involved in the control of movements and could therefore induce confounding effects on motor behavior analyses.

      We thank the reviewer for suggesting to investigate in more details the interneuron populations that display TrkC receptors, which is now included in the revised version of the manuscript (Figure 3E-G).

      (4) The rationale for following up on NT3 seems to be the chick electroporation experiments; however, no changes in distribution are shown in those experiments, and only a very minor decrease in Chx10 interneurons. Shouldn't NT3 overexpression lead to substantial decreases in IN numbers according to the authors' model? The "data not shown", which presumably refers to distribution, would be important to show here, to further support this rationale.

      Chicken spinal cord electroporation only enables to study spinal cord development for a few days, given the high mortality rate observed after longer incubation. At the stage we collected the electroporated embryos for analyses, interneuron migration has barely been initiated, and distribution cannot be studied yet. Consistently, we are not aware of any report of interneuron distribution analysis in electroporated chicken embryonic spinal cord, as compared to mouse embryos [1-5].

      (5) The idea that NT3 downregulation causes an increase in IN numbers is not intuitive. Also, considering the DTA experiments in Figure 1, showing that MN ablation leads to a decrease in several IN subtypes and no changes in V2a neurons. It would be helpful for the reader if the authors could synthesize their results in the discussion and reconcile their experimental findings.

      We agree, this has now been included in the Discussion section of the revised manuscript.

      Reviewer #3 (Public review):

      This manuscript aims to investigate cell extrinsic mechanisms that regulate the differentiation and distribution of interneuron types in the spinal cord. The authors demonstrate that the loss of motor neurons leads to changes in the number and distribution of different interneuron types, specifically V0v, V1, and V2b (but not V2a). The authors then hypothesize that this phenotype may be controlled by the action of Onecut (OC) transcription factors in motor neurons. Conditional knockout of OC1 + OC2 in motor neurons using Olig2-Cre, however, does not lead to significant changes in the numbers of V1, V2a, and V2b interneurons, although there is a change in their spatial distribution. While the authors do not check V0v neurons in OC mutants, they do check V2c, which show a reduction in number and change in distribution. Why the same neurons are not checked across experiments is unclear. The authors then analyze existing RNA-seq data to identify factors that could be mediating the effects of the OC factors in motor neurons. They identify Ntf3 as a candidate and confirm that it is upregulated in OC mutants. Conditional loss of function of Ntf3 (Olig2-Cre) leads to increases in V1, V2a, and V2b (but not V2c) interneurons and changes in the distribution of all four interneuron types. Finally, the authors demonstrate that these Ntf3 conditional mutants have major defects in motor function.

      The conclusions of this manuscript are not well supported by the data for the reasons listed below, making it difficult to assess the impact of this work on the field.

      (1) The manuscript relies heavily on quantifying numbers and the spatial distribution of interneuron populations. However, these do not seem to be consistent in control animals across experiments, making it difficult to interpret any changes observed in genetic manipulations. Specifically, in Figures 2 and 4, the same markers are being used to quantify V1, V2a, V2b, and V2c interneurons in controls vs. OC (Figure 2) or Ntf3 (Figure 4) conditional knockouts, but the numbers of neurons and their distribution in control animals are variable between these two figures. For example, there seems to be a mean of >300 V1 neurons in E12.5 brachial sections of Fig. 2 controls, but this number is <150 in Fig. 4 controls. The cell distribution scoring is similarly variable between these controls without any explanation. The same is true for E14.5 controls used in Figure S1 vs. Figure S3.

      We also noticed that quantifications and distributions of the different analyzed interneuron populations in the control embryos showed some differences between the cOc1Oc2<sup>-/-</sup> and in the cNtf3<sup>-/-</sup> lines. Several parameters can account for this observation. First, this study has been conducted over a period of 15 years, different investigators each contributing to different steps of the analysis. Therefore, the interneuron distribution analyses in these two mouse lines were not conducted by the same researchers. Second, the genetic background of these two lines is not identical, impacting both the duration of the gestation (hence, the embryonic stage of the performed analyses, even if the embryos were collected on the same gestation day) and possibly the distribution of some interneuron populations. Third, because of evolutions in the availability of the primary antibodies used to label the interneuron populations of interest, the same antibodies were not used throughout the study, as stated in the Materials and Methods section for Chx10, Gata3 and Sox1, although the same antibody was used by the same investigator to label the same interneuron population in each mouse line at each developmental stage.

      (2) Neurotrophic factors generally promote neuronal survival. However, in this study, the loss of Ntf3 leads to increased numbers of interneurons. This finding is in disagreement with previous observations in slice cultures of spinal cords, as stated in the discussion. This discrepancy makes it even more important that the cell counts reported in the figures discussed above are robust.

      Considering that neurotrophic factors only support neuronal survival would strongly neglect their important function in neuronal differentiation, which has been broadly demonstrated. Severe immunotoxic ablation of motor neurons or anti-serum blockade of Ntf3 activity severely depleted inhibitory, but not excitatory, interneurons in a highly apoptotic-prone organotypic culture model of embryonic rat spinal cord slices, which was rescued by Ntf3 in the first model [6]. Opposite results were obtained in vivo by other researchers using mouse models lacking almost all MNs due to the elimination of skeletal muscles, where the number of spinal INs remained unaffected [7,8]. Combined to our results, these in vivo observations suggest that Ntf-3 is involved in interneuron differentiation rather in their survival. Consistently, Ntf3 has been shown to promote neuronal differentiation [9].

      (3) The claim that phenotypes are non-cell autonomously driven by motor neurons is not well supported. In Olig2-Cre conditional knockouts of Onecut and Ntf3, there is no confirmation that the loss of these factors is specific to motor neurons. Therefore, it cannot be ruled out that other cell populations may be mediating the phenotypes.

      Combined conditional inactivation of Oc1 and Oc2 has been reported in [10]. Conditional inactivation of Ntf3 only impacts motor neurons as it is the only cell population in the ventral spinal cord wherein this factor is produced (this study and [11-13]). Furthermore, Olig2-Cre has been shown to be active in motor neurons and in V3 interneurons (see for example [14], which, for this reason, have not been studied in the frame of this project as stated in the manuscript.

      (4) The claim that interneuron development is regulated by OC control of Ntf3 expression in motor neurons is not well supported. The authors show that loss of OC1/2 leads to an increase in Ntf3 expression in motor neurons. If this pathway were controlling interneurons, loss of OC function and overexpression of Ntf3 would have the same phenotype, which is not the case. Additionally, it would also be expected that loss of OC function and loss of Ntf3 function would have inverse phenotypes, which is also not the case. The phenotypes from OC loss of function and Ntf3 loss of function seem distinct from one another. The authors state that too little and too much Ntf3 are both bad for interneuron development, but there is no data to support their claim that OC1/2 mutants have altered interneuron development because of higher Ntf3 expression.

      In this study, we did not intend to demonstrate that Onecut transcription factors exert their non-cell autonomous action on spinal interneuron development by regulating Ntf3 expression, and we do not state in the manuscript that this is the case. We only show that Onecut factors and Ntf3, the expression of which they regulate, contribute to the non-cell autonomous regulation of spinal interneuron development by the motor neurons. We are convinced that Onecut factors could regulate multiple independent factors and pathways involved in extrinsic regulation of interneuron development. This possibly also includes, as demonstrated in cell culture for multiple homeoproteins including human Onecut factors, the intercellular transfer of the Onecut homeoproteins during spinal cord development, a process that we are currently investigating.

      (5) It is not clear that interneurons being studied express the Ntf3 receptor TrkC, which makes it difficult to assess whether changes in Ntf3 signaling are directly responsible for the phenotype.

      We thank the reviewer for suggesting to investigate in more details the interneuron populations that display TrkC receptors, which is now included in the revised version of the manuscript (Figure 3E-G).

      (6) While the behavioral phenotypes are consistent with Ntf3 playing a role in motor circuits, there is no evidence to suggest that Ntf3's influence on premotor interneurons being studied is driving or contributing to this phenotype, as discussed by the authors.

      We agree, the motor behavior changes that we recorded in Ntf3 conditional mutant mice are, as stated, “consistent with the hypothesis that Ntf3 produced by MNs is required to generate locomotor circuits with properly coordinated activity” but do not demonstrate a direct causal relationship. However, investigating the intrinsic activity of the spinal locomotor circuits was beyond the scope of this study.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) Figure 3: A more comprehensive overview of the RNA-seq results would be helpful to see how much gene expression is shifted as a result of OC loss (e.g., volcano plot, pathway enrichment analysis).

      We agree, this has now been added in Figures 3B and S3 of the revised manuscript.

      (2) Figure 2, 4: How does the spatial distribution of motor neurons change?

      Describing the distribution of motor neurons is highly challenging given that the expression of markers that are collectively required to label all the motor neuron subset, including Foxp1, Hb9 and most significantly Isl1, is altered when Onecut expression is modified. Furthermore, changes in motor neuron distribution could constitute consequences of alterations in pre-motor interneuron numbers or distributions. However, as far as we can observe, motor neuron distribution was not obviously modified in the studied mutants.

      (3) Figure S2: Why is Ntf3 expression so limited compared to Isl1-expressing motor neurons? An explanation would be helpful.

      As stated in the Material and Methods section and in the legend of Figure S4 of the revised manuscript, expression after chicken embryonic spinal cord electroporation is transient and maximal reporter gene expression, including GFP, can usually only be visualized 24h after electroporation, but not at later stages. Forty-height hours after electroporation, only residual expression can still be detected in a few targeted cells.

      Reviewer #3 (Recommendations for the authors):

      (1) Changes in interneuron numbers are attributed to defects in differentiation. However, the possibility that these could be caused by changes in cell death has not been ruled out.

      Absence of increased cell death had been demonstrated previously for Onecut mutant embryos [15] and has been verified now in conditional Ntf3 mutant embryos (Figure S5). Both information have been included in the text.

      (2) In Figures 2 and 4, it is unclear if the images of the spinal cords are from brachial, thoracic, or lumbar levels. As the three levels frequently show different phenotypes, it is difficult to match the immunostaining with the reported quantification.

      All the pictures show thoracic regions of the spinal cord, as now stated in the figure legends of the revised version of the manuscript.

      (3) Statistics are missing in the cell distribution plots in Figure S1.

      This is not the case in our version of the pdf file.

      (4) In experiments where motor neurons are eliminated with Olig2-Cre/DTA, the authors write that progenitors are not affected without showing data. These data should be included because i) Olig2 is expressed in progenitors, so it would be expected that motor neuron progenitors are lost or reduced in this mouse, and ii) showing no changes in progenitors is crucial to the claim that postmitotic motor neurons are affected interneuron populations.

      We agree, these data are now included as Figure S1 of the revised version of the manuscript.

      (1) Harris, A. et al. Onecut factors and Pou2f2 regulate the distribution of V2 interneurons in the mouse developing spinal cord. Front Cell Neurosci 13 (2019). https://doi.org/10.3389/fncel.2019.00184

      (2) Kabayiza, K. U. et al. The Onecut Transcription Factors Regulate Differentiation and Distribution of Dorsal Interneurons during Spinal Cord Development. Front Mol Neurosci 10, 157 (2017). https://doi.org/10.3389/fnmol.2017.00157

      (3) Deska-Gauthier, D. et al. Embryonic temporal-spatial delineation of excitatory spinal V3 interneuron diversity. Cell Rep 43, 113635 (2024). https://doi.org/10.1016/j.celrep.2023.113635

      (4) Bikoff, J. B. et al. Spinal Inhibitory Interneuron Diversity Delineates Variant Motor Microcircuits. Cell 165, 207–219 (2016). https://doi.org/10.1016/j.cell.2016.01.027

      (5) Hayashi, M. et al. Graded Arrays of Spinal and Supraspinal V2a Interneuron Subtypes Underlie Forelimb and Hindlimb Motor Control. Neuron 97, 869–884 e865 (2018). https://doi.org/10.1016/j.neuron.2018.01.023

      (6) Bechade, C., Mallecourt, C., Sedel, F., Vyas, S. & Triller, A. in J Neurosci Vol. 22 8779–8784 (2002).

      (7) Grieshammer, U., Lewandoski, M., Prevette, D., Oppenheim, R. W. & Martin, G. R. Muscle-specific cell ablation conditional upon Cre-mediated DNA recombination in transgenic mice leads to massive spinal and cranial motoneuron loss. Dev Biol 197, 234–247 (1998). https://doi.org/10.1006/dbio.1997.8859

      (8) Kablar, B. & Rudnicki, M. A. Development in the absence of skeletal muscle results in the sequential ablation of motor neurons from the spinal cord to the brain. Dev Biol 208, 93–109 (1999). https://doi.org/10.1006/dbio.1998.9184

      (9) Dutton, R., Yamada, T., Turnley, A., Bartlett, P. F. & Murphy, M. Regulation of spinal motoneuron differentiation by the combined action of Sonic hedgehog and neurotrophin 3. Clin Exp Pharmacol Physiol 26, 746–748 (1999). https://doi.org/10.1046/j.1440-1681.1999.03108.x

      (10) Toch, M. et al. Onecut-dependent Nkx6.2 transcription factor expression is required for proper formation and activity of spinal locomotor circuits. Sci Rep 10, 996 (2020). https://doi.org/10.1038/s41598-020-57945-4

      (11) Buck, C. R., Seburn, K. L. & Cope, T. C. Neurotrophin expression by spinal motoneurons in adult and developing rats. J Comp Neurol 416, 309–318 (2000). https://doi.org/10.1002/(sici)1096-9861(20000117)416:3<309::aid-cne3>3.0.co;2-u

      (12) Henderson, C. E. et al. Neurotrophins promote motor neuron survival and are present in embryonic limb bud. Nature 363, 266–270 (1993). https://doi.org/10.1038/363266a0

      (13) Usui, N. et al. Role of motoneuron-derived neurotrophin 3 in survival and axonal projection of sensory neurons during neural circuit formation. Development 139, 1125–1132 (2012). https://doi.org/10.1242/dev.069997

      (14) Debrulle, S. et al. Vsx1 and Chx10 paralogs sequentially secure V2 interneuron identity during spinal cord development. Cell Mol Life Sci 77, 4117–4131 (2020). https://doi.org/10.1007/s00018-019-03408-7

      (15) Roy, A. et al. Onecut transcription factors act upstream of Isl1 to regulate spinal motoneuron diversification. Development 139, 3109–3119 (2012). https://doi.org/10.1242/dev.078501

    1. eLife Assessment

      This valuable study is a rigorous and comprehensive anatomical description of populations of spinal-projecting neurons in the brain of the larval zebrafish. The compelling data reveal a far more complete and robust visualization of these populations than past work. The scholarship is also notable, featuring thoughtful contextualization of the findings with respect to both non-mammalian and mammalian literature.

    2. Reviewer #2 (Public review):

      Summary:

      Vertebrate spinal cord receives inputs from many supraspinal regions. The authors used optical backfilling to trace neurons in the zebrafish larval brain sending axons to the spinal cord . With two-photon microscopy, they managed to render a comprehensive 3D map of these neurons and drew homologs with mammalian brain structures.

      Strengths:

      The main strength lies in the precise 3D mapping. The fact that most of previously reported neuron groups have been confirmed by their approach is a solid endorsement of their methodology.

      This study provides a comprehensive alternative anatomical reference framework for studying individual groups of supraspinal neurons with projections to the zebrafish spinal cord.

      Weaknesses:

      The backfilling approach does not reveal the full trajectories of axons, which is available to some degree by ZExplorer Atlas.

    3. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      The authors have achieved an excellent, thorough anatomical characterization of all spinal projecting neurons in the larval zebrafish. The comprehensive nature of their labeling approach and their quantification will make this work an instant reference benchmark for a wide number of zebrafish researchers. In addition, the scholarly approach in comparisons with other work in and outside of fish makes the manuscript valuable to researchers outside the field who would like to know how to translate between zebrafish and mouse terminologies.

      Strengths:

      The figures are clear and easy to follow. The literature review is impressive. The authors are careful to note the few limitations of their approach (eg the absence of Mauthner cell labeling and associated large neuron weak label). The manuscript does the whole field a major service.

      Weaknesses:

      No weaknesses were identified by this reviewer.

      We deeply thank reviewer #1 for recognizing the importance of our work.

      Reviewer #2 (Public review):

      Summary:

      The vertebrate spinal cord receives inputs from many supraspinal regions. The authors used optical backfilling to trace neurons in the zebrafish larval brain sending axons to the spinal cord. With two-photon microscopy, they managed to render a comprehensive 3D map of these neurons and drew homologs with mammalian brain structures.

      Strengths:

      The main strength lies in the precise 3D mapping. The fact that most of the previously reported neuron groups have been confirmed by their approach is a solid endorsement of their methodology. This study provides a comprehensive alternative anatomical reference framework for studying individual groups of supraspinal neurons with projections to the zebrafish spinal cord.

      Weaknesses:

      The whole approach could be enhanced by counter-staining their preparation to profile brain structures, including many nuclei more precisely.

      Also, the backfilling approach does not reveal the full trajectories of axons, which is already available to some degree by ZExplorer Atlas.

      We thank Reviewer #2 for the attention given to important details. As you will read below, our approach needed to be implemented on live larval zebrafish for the PA GFP to fluoresce only when photactivated. Although counterstains on fixed samples could have been done, it would have been difficult to trace it back in a fixed (and deformed) sample where all neurons appear GFP-positive. We wrote this publication so it can serve the field best before the release of full brain connectome in 2027. Further molecular and histological characterization will be the topic of a subsequent study. Below we will individually address the issues stated by the reviewer.

      Reviewer #2 (Recommendations for the authors):

      (1) The power of 800 nm laser: 1 mW in Figure 1a, 10 mW stated in the method text.

      Thanks for catching this, this is now corrected to 1 mW in the revised manuscript.

      (2) Figure 2c: laterality density (e.g., 90:40 by neuron numbers) is already quite clear. The laterality index is less informative if not causing confusion. Take POA as an example; shouldn't the value be close to 1, rather than -0.8 based on your formula?

      The laterality density plot shows the anatomical spatial distribution of spinal descending neurons relative to the midline, whereas the laterality index is a scalar summary of the relative neuron counts on each side, independent of spatial position. For POA, we observed after pooling across animals 26 ipsilateral versus 1 contralateral neurons, which gives an LI close to -0.8 (after bootstrapping), indicating strong ipsilateral dominance. The density plot additionally shows that these neurons cluster close to the midline. We view the two measures as complementary: one quantifies laterality, the other reflects its spatial organization.

      (3) Line 286, check grammar.

      Corrected to "Using the ZExplorer atlas (Du et al., 2025), which contains thousands of single-neuron tracings in the larval zebrafish brain, we searched for telencephalic neurons projecting to the spinal cord."

      (4) Add discussion on comparison between your approach, conventional backfill tracing method, singlecell tracing by dye or GFP, and, more importantly, ZExplorer Atlas.

      We believe this comparison is sufficiently addressed in the Introduction and Discussion.

      (5) Line 346, add "in bi" after "scale bar".

      Corrected, thank you for spotting this.

      (6) Figure 4, change label ICN to INC in c and d.

      We apologize for this confusion, all of them are now corrected in the revised manuscript.

      (7) Line 389, change to "ventral (top) to dorsal (bottom)". Try to use the same order for all figures, i.e., dorsal images on top and ventral ones at the bottom.

      Corrected for the specific line and now all figures follow the same convention.

      (8) Figure 7b, add stack location diagrams.

      Added. Thank you very much for suggesting an improvement to the figure readability.

      (9) Figure 7c and other figure panels, suggest changing the word "medial" to "middle" since medial/lateral is usually used to describe sagittal locations.

      Good point: now corrected as suggested.

      (10) Why was DAPI counter-staining not carried out? This would help the authors to better draw outlines for different nuclei. It also provides important data on the proportion of SPNs within each nucleus.

      We agree with the reviewer that determining the proportion of SPNs within each nucleus is an important question; however, properly addressing this requires an independent, cytoarchitecture-based definition of nucleus boundaries, a substantial project that we consider outside the scope of the present study.

      DAPI labels cell nuclei generally but carries limited information relevant to classical neuroanatomical delineation of nuclei. Methods such as Nissl staining are better suited to this purpose, as they reveal the cytoarchitectonic characteristics of neurons. As mentioned in the Introduction and Discussion, we instead based our nuclei definitions on the strong evolutionary conservation of spinal-projecting neuron distributions, which we used as a proxy for delineating the different nuclei.

      (11) The photoactivation is restricted to the rostral spinal cord. Therefore, how far the axons project throughout the spinal cord cannot be assessed with the current photo-activation method.

      We agree with the reviewer: because photoactivation was restricted to the rostral spinal cord, our current data cannot resolve how far individual axons project caudally. However, all evolutionary comparisons in this study were performed across vertebrates using similarly rostrally-labeled SPNs, ensuring consistency in our cross-species analysis. We therefore do not believe that this limitation undermines our initial characterization of the SPN populations.

      We agree, however, that extending photoactivation to multiple spinal levels will be valuable for future work characterizing and refining the nuclei proposed in this work. Some of this effort is carried for vsx2+ neurons only by Jia et al., under revision in Nature Communications.

      (12) Line 544, if it is the spinal trigeminal tract, why does there appear to be some feint soma staining?

      We apologize for the confusion. We always meant to say trigeminal nucleus and not its tract. Text and figure legend are now corrected.

      Reviewer #3 (Public review):

      In this study, the authors aim to provide the most comprehensive and detailed topographic map to date of spinal projection neurons in the larval zebrafish brain. They achieve this by retrogradely photoactivating, in the rostral spinal cord, a photoconvertible GFP expressed pan-neuronally, and by constructing a template larval zebrafish brain atlas to regionalize the location of all labeled somata across the brain. The labeling strategy, together with the chosen animal model, provides strong support for the completeness of the dataset. The generation of a standardized anatomical atlas establishes a rigorous framework for analysis. Molecular and anatomical evidence suggesting evolutionary conservation of selected regions of interest appears solid.

      Overall, the authors successfully achieve their aim. By generating this atlas of spinal projection neurons, they provide not only an anatomical framework of the regions involved, but also an important reference for improving the orientation and regionalization of the zebrafish brain, which has historically been difficult to define. This work may serve as a valuable resource for future evolutionary, developmental, and comparative studies of spinally projecting neuronal populations implicated in diverse functions.

      We deeply thank Reviewer #3 for recognizing the impact of our work and benefit for the community at large, which has always been our intention.

    1. eLife Assessment

      This is an important study with implications for signal transduction, breast cancer, and metastasis research. The study uses cell lines from the innovative HER2BOW model mouse to compare and contrast properties of mouse mammary tumors induced by wt-HER2, dex16-HER2, and p95-HER2. The study involves mechanistic approaches that are convincing for distinguishing signaling from dex16-HER2 and p95-HER2; this will be of interest to the cancer field.

    2. Reviewer #1 (Public review):

      Summary:

      The study by Fernandes et al used multiple types of experiments to study the increased metastatic activity conferred by p95-HER2 and presented evidence that biased downstream signaling was engaged by p95-HER2. Further studies demonstrated crucial phosphorylation sites and downstream transcription targets. Overall, this study will contribute to increased understanding of HER2 biology and breast cancer metastasis.

      Strengths:

      Multiple types of experiments to assess the increased invasion property by p95-HER2. Solid evidence was provided that p95-HER2 elicits a different transcription program, enhancing EMT. Convincing results were achieved in knockdown studies on crucial genes such as MRTFA and TGFB1I1.

      Weaknesses:

      To enhance the quality of this study, several points need to be addressed.

      (1) First, as the background to this study, the authors stated in the introduction that different isoforms of HER2 lead to different invasive properties. Is this conclusion based on clinical observation or animal models? This needs to be specified to help readers grasp the importance of the paper.

      (2) Second, this study is mostly performed on three cell lines generated from three tumor models, WT, d16, and p95. Were multiple cell lines generated from the same tumor model (e.g., p95), and how similar are these cell lines if they are from the same tumor model?

      (3) Third, Figure 2 presented data from a very interesting experiment. However, the overall conclusion of this experiment is hard to grasp from the text.

    3. Reviewer #2 (Public review):

      Summary:

      This is a significant study with implications for signal transduction, breast cancer, and metastasis research. The study uses cell lines from the innovative HER2BOW model mouse to compare and contrast properties of mouse mammary tumors induced by wt HER2, dex16 HER2, and p95 HER2. The linkage between p95 Y1139 and TGFB1I1, as well as MRTFA, is very interesting. The study involves mechanistic approaches to distinguish signaling from dex16 HER2 and p95 HER2 that will be well received by the field.

      Strengths:

      The study describes the development of novel cell lines for mouse mammary tumors induced by different forms of HER2. In vitro analysis includes a wide variety of assays, including high-resolution fluorescent imaging and tracing of cell movement/ECM degradation, and single-cell RNA-seq analysis. Several approaches are used to confirm p95 HER2 pY1139-induced signaling through nuclear MRTFA and TGFB1I1. shRNA and CRISPR/Cas9 were used to confirm signaling through this pathway.

      Weaknesses:

      The authors have only described one cell line for each HER2 form, a weakness which they acknowledge. The fact that these lines mimic characteristics that were described in their previous work on tumors induced by wt HER2 vs. dex16 HER2 vs. p95 HER2 is, however, supportive of their conclusions. In addition, the specific connection between p95 and nuclear MRTFA is confirmed in HEK cells. The manuscript could be improved with some discussion on how p95 p-Y1139 is likely to signal (what binds to this site?)

    4. Reviewer #3 (Public review):

      Summary:

      The study aims to explain why different HER2 isoforms produce distinct cancer-cell phenotypes, with a particular focus on the poorly understood mechanisms by which p95-HER2 promotes the invasive and metastatic behaviors of HER2-positive breast cancer. The authors propose that functional selectivity at the HER2 receptor level, particularly signaling through Y1139, preferentially engages an MRTFA-TGFB1I1 transcriptional/cytoskeletal program, thereby shifting p95-HER2-expressing cells toward individual motility, matrix degradation, invasion, and metastasis rather than simply proliferation.

      Strengths:

      A major strength is the multi-level experimental strategy. Rather than relying on a single invasion assay, the authors integrate phenotypic profiling, single-cell RNA-seq, imaging of MRTFA nuclear localization, protein validation, cytoskeletal analysis, matrix-degradation assays, transwell invasion, 3D spheroid/organoid assays, and orthotopic transplantation. This provides relatively coherent evidence that p95-HER2 cells occupy a distinct biological state characterized by individual motility and invasion, accompanied by a myogenic/contractile transcriptional program and increased MRTFA nuclear localization. Another strong aspect is the attempt to connect the downstream phenotype to a specific HER2 phosphorylation site. The authors identify Y1139 as a candidate determinant of the "go" phenotype and show that mutation of Y1139 to phenylalanine eliminates MRTFA translocation as well as the associated motility and invasion phenotypes

      Weaknesses:

      The main limitation is that the evidence for the proposed signaling mechanism is stronger for functional dependence than for direct molecular mechanism. The data establish that Y1139, MRTFA, and TGFB1I1 are required for the p95-HER2 invasive phenotype, but they do not appear to fully establish how phosphorylation at Y1139 mechanistically couples p95-HER2 to MRTFA activation. In particular, a direct biochemical link between Y1139 and the machinery controlling actin polymerization/MRTFA nuclear translocation would make the proposed pathway substantially more convincing.

      A second limitation is the model system used in the study. Much of the mechanistic work relies on three particular engineered HER2 isoform-expressing cell lines, while the in vivo experiments use NOD/SCID mice. This is useful for testing tumor-cell intrinsic invasion and metastasis, but it limits conclusions about the generality of the functional regulation across different cell contexts, and also limits the understanding of how the pathway operates in the intact immune microenvironment. This is especially relevant because the manuscript itself places p95-HER2 in the broader context of immune evasion and notes that MRTFA can regulate PD-L1; however, the current models cannot adequately establish whether the proposed pathway contributes to immune-mediated metastatic progression in vivo.

      Finally, the distinction between "go" and "grow" signaling is conceptually attractive, but the extent to which it represents a general property of HER2-positive human breast cancers remains less certain. The study provides strong experimental evidence for functional selectivity in the models examined, but additional validation in independent patient-derived models and clinically relevant tumors would be needed before generalizing this mechanism across HER2-positive disease.

    5. Author response:

      Reviewer #1 (Public review):

      Summary:

      The study by Fernandes et al used multiple types of experiments to study the increased metastatic activity conferred by p95-HER2 and presented evidence that biased downstream signaling was engaged by p95-HER2. Further studies demonstrated crucial phosphorylation sites and downstream transcription targets. Overall, this study will contribute to increased understanding of HER2 biology and breast cancer metastasis.

      Strengths:

      Multiple types of experiments to assess the increased invasion property by p95-HER2. Solid evidence was provided that p95-HER2 elicits a different transcription program, enhancing EMT. Convincing results were achieved in knockdown studies on crucial genes such as MRTFA and TGFB1I1.

      We thank the reviewer for this positive feedback on our manuscript.

      Weaknesses:

      To enhance the quality of this study, several points need to be addressed.

      (1) First, as the background to this study, the authors stated in the introduction that different isoforms of HER2 lead to different invasive properties. Is this conclusion based on clinical observation or animal models? This needs to be specified to help readers grasp the importance of the paper.

      We thank the reviewer for bringing this to our attention; we will add additional language to emphasize and explain prior findings referenced in the introduction establishing HER2 isoforms’ different invasive properties. The increased invasiveness of both d16 and p95 isoforms of HER2 has been established both in animal models and in clinical studies (1-6). We summarize these studies below:

      (1) Expression of the delta16 splice variant isoform has been shown in human cell lines to be more oncogenic than the WT HER2 isoform (7), and associated with different transcriptional profile including increased expression of cancer metastasis and cancer stem cell-related genes (4). Clinically, patients with d16HER2-positive breast cancer have higher rates of lymph node metastases than those with d16HER2-negative breast cancer (8). Castagnoli et al (9) provides an overview of preclinical and clinical data on d16 in HER2+ cancers.

      (2) The p95 isoform of HER2 is associated with increased metastasis (2) and worse outcomes (1, 10) in HER2+ breast cancer patients. Preclinical models show that p95-HER2 (also known as 611-CTF) activates a transcriptional program that promotes invasion and metastasis (3), part of the basis for our interest in HER2 isoform biology.

      Moreover, our group has previously detailed different tumor growth and dissemination characteristics of WT, d16, and p95 HER2 isoforms in our HER2 Crainbow mouse model which expresses all 3 of these isoforms in the mammary epithelium (5).

      Both d16 and p95 tumors in Her2Crainbow mice have the ability to progress to overt metastasis, unlike WT HER2 in this model (6). During early stages of malignant transformation prior to palpable tumor formation, p95 cells disseminated to the lung were 5x more numerous than d16 cells in the lung (despite more proliferation of d16 cells in the mammary gland at that timepoint). This is the foundation on which we built this current work, to study what could promote these different proliferation, dissemination, and metastasis behaviors in different HER2 isoforms.

      (2) Second, this study is mostly performed on three cell lines generated from three tumor models, WT, d16, and p95. Were multiple cell lines generated from the same tumor model (e.g., p95), and how similar are these cell lines if they are from the same tumor model?

      Our lab has generated a number of tumor cell lines derived from individual tumors developed in different HER2 Crainbow mice. We hypothesize, based on preliminary data in these cell lines as well as our previously reported data examining phenotypes of WT, d16, and p95 tumors (5, 6), that different clones will share core features such as invasive properties. In order to strengthen the generalizability of our conclusions, we intend to perform transwell assays, matrix degradation assays, and western blots to examine expression of Mrtfa and Tgfb1i1 in additional HER2 Crainbow-derived cell lines to validate if our findings hold true across cell lines derived from different tumors.

      (3) Third, Figure 2 presented data from a very interesting experiment. However, the overall conclusion of this experiment is hard to grasp from the text.

      We thank the reviewer for this comment. We will revise the text accompanying Figure 2 to provide further detail on the interpretation of the PCA plots and clarify the overall conclusions by adding the following text:

      Vectors indicate variable loadings, where vector direction corresponds to increasing values of the variable and vector length reflects the magnitude of the variable’s contribution to the displayed principal components. For example, HCR-WT populations cluster toward lower values of PC1 and higher values PC2, notably in the direction of higher self-correlation indicating an association with greater spatial clustering. HCR-16 and HCR-95 cells cluster similarly and are driven by their similarity in speed, MSD, persistence, and individual behavior (low self-correlation and high proportions of cells with zero neighbors), whereas HCR-WT is distinct for its collective cell behavior (high self-correlation) (Figure 2E). This separation indicates potentially distinct kinematic and spatial organization compared to HCR-16 and HCR-95 cells.

      Reviewer #2 (Public review):

      Summary:

      This is a significant study with implications for signal transduction, breast cancer, and metastasis research. The study uses cell lines from the innovative HER2BOW model mouse to compare and contrast properties of mouse mammary tumors induced by wt HER2, dex16 HER2, and p95 HER2. The linkage between p95 Y1139 and TGFB1I1, as well as MRTFA, is very interesting. The study involves mechanistic approaches to distinguish signaling from dex16 HER2 and p95 HER2 that will be well received by the field.

      Strengths:

      The study describes the development of novel cell lines for mouse mammary tumors induced by different forms of HER2. In vitro analysis includes a wide variety of assays, including high-resolution fluorescent imaging and tracing of cell movement/ECM degradation, and single-cell RNA-seq analysis. Several approaches are used to confirm p95 HER2 pY1139-induced signaling through nuclear MRTFA and TGFB1I1. shRNA and CRISPR/Cas9 were used to confirm signaling through this pathway.

      We thank the reviewer for these positive comments.

      Weaknesses:

      The authors have only described one cell line for each HER2 form, a weakness which they acknowledge. The fact that these lines mimic characteristics that were described in their previous work on tumors induced by wt HER2 vs. dex16 HER2 vs. p95 HER2 is, however, supportive of their conclusions. In addition, the specific connection between p95 and nuclear MRTFA is confirmed in HEK cells. The manuscript could be improved with some discussion on how p95 p-Y1139 is likely to signal (what binds to this site?)

      We thank the reviewer for this assessment, and we agree that adding additional cell lines derived from different HER2 Crainbow mouse tumors would strengthen the conclusions drawn in this manuscript. As mentioned in response to Reviewer #1, we intend to perform transwell assays, matrix degradation assays, and western blots to examine expression of Mrtfa and Tgfb1i1 in additional HER2 Crainbow-derived cell lines to validate if our findings hold true across cell lines derived from different tumors.

      With regards to p95 p-Y1139 signaling, previous work by Dankort et al. (11, 12) established that tyrosine autophosphorylation site Y1144 on rat neu (equivalent to Y1139 in human HER2) promoted lung metastasis in animal models, and that this site was necessary and sufficient for direct binding of Grb2.

      We intend to clarify and discuss links between Y1139 on p95 HER2 and the nuclear translocation of MRTFA.

      Reviewer #3 (Public review):

      Summary:

      The study aims to explain why different HER2 isoforms produce distinct cancer-cell phenotypes, with a particular focus on the poorly understood mechanisms by which p95-HER2 promotes the invasive and metastatic behaviors of HER2-positive breast cancer. The authors propose that functional selectivity at the HER2 receptor level, particularly signaling through Y1139, preferentially engages an MRTFA-TGFB1I1 transcriptional/cytoskeletal program, thereby shifting p95-HER2-expressing cells toward individual motility, matrix degradation, invasion, and metastasis rather than simply proliferation.

      Strengths:

      A major strength is the multi-level experimental strategy. Rather than relying on a single invasion assay, the authors integrate phenotypic profiling, single-cell RNA-seq, imaging of MRTFA nuclear localization, protein validation, cytoskeletal analysis, matrix-degradation assays, transwell invasion, 3D spheroid/organoid assays, and orthotopic transplantation. This provides relatively coherent evidence that p95-HER2 cells occupy a distinct biological state characterized by individual motility and invasion, accompanied by a myogenic/contractile transcriptional program and increased MRTFA nuclear localization. Another strong aspect is the attempt to connect the downstream phenotype to a specific HER2 phosphorylation site. The authors identify Y1139 as a candidate determinant of the "go" phenotype and show that mutation of Y1139 to phenylalanine eliminates MRTFA translocation as well as the associated motility and invasion phenotypes

      We thank the reviewer for these positive comments on our manuscript.

      Weaknesses:

      The main limitation is that the evidence for the proposed signaling mechanism is stronger for functional dependence than for direct molecular mechanism. The data establish that Y1139, MRTFA, and TGFB1I1 are required for the p95-HER2 invasive phenotype, but they do not appear to fully establish how phosphorylation at Y1139 mechanistically couples p95-HER2 to MRTFA activation. In particular, a direct biochemical link between Y1139 and the machinery controlling actin polymerization/MRTFA nuclear translocation would make the proposed pathway substantially more convincing.

      We thank the reviewer for these insightful comments. We agree that establishing a mechanistic link between p95 Y1139 and MRTFA nuclear translocation would strengthen the conclusions in this study. As such, we intend to perform Rho activation assays and western blotting for proteins in HER2 signaling cascades to explore the signaling link between p95 Y1139 and MRTFA. Additionally, we plan to treat cells with RhoA/ROCK inhibitors to explore our hypothesis that Y1139 acts through RhoA activation to ultimately alter MRTFA translocation.

      A second limitation is the model system used in the study. Much of the mechanistic work relies on three particular engineered HER2 isoform-expressing cell lines, while the in vivo experiments use NOD/SCID mice. This is useful for testing tumor-cell intrinsic invasion and metastasis, but it limits conclusions about the generality of the functional regulation across different cell contexts, and also limits the understanding of how the pathway operates in the intact immune microenvironment. This is especially relevant because the manuscript itself places p95-HER2 in the broader context of immune evasion and notes that MRTFA can regulate PD-L1; however, the current models cannot adequately establish whether the proposed pathway contributes to immune-mediated metastatic progression in vivo.

      We also acknowledge the limitations of relying on mostly in vitro assays and in vivo experiments in an immunodeficient environment, and will more explicitly address this in our discussion. The interplay between p95-HER2 and the immune system is being explored in a parallel project that will be published separately in the future; the focus of the current manuscript is the cell-intrinsic signaling mechanisms that contribute to p95 invasiveness. Points about immune evasion that were brought up in the introduction will be moved to the discussion section, to make it clear that the current study does not address the immunocompetent environment but that we wish to further investigate the results we report in this manuscript in that context as well.

      Finally, the distinction between "go" and "grow" signaling is conceptually attractive, but the extent to which it represents a general property of HER2-positive human breast cancers remains less certain. The study provides strong experimental evidence for functional selectivity in the models examined, but additional validation in independent patient-derived models and clinically relevant tumors would be needed before generalizing this mechanism across HER2-positive disease.

      With respect to validating findings in human samples, we intend to utilize human HER2+ breast cancer cell lines and/or patient-derived xenografts to investigate the extent to which our described mechanism is relevant in human HER2+ breast cancer.

      References:

      (1) Sáez R, Molina MA, Ramsey EE, Rojo F, Keenan EJ, Albanell J, et al. p95HER-2 Predicts Worse Outcome in Patients with HER-2-Positive Breast Cancer. Clinical Cancer Research. 2006;12:424–31.

      (2) Molina MA, Saez R, Ramsey EE, Garcia-Barchino MJ, Rojo F, Evans AJ, et al. NH(2)-terminal truncated HER-2 protein but not full-length receptor is associated with nodal metastasis in human breast cancer. Clin Cancer Res. 2002;8:347–53.

      (3) Pedersen K, Angelini PD, Laos S, Bach-Faig A, Cunningham MP, Ferrer-Ramon C, et al. A naturally occurring HER2 carboxy-terminal fragment promotes mammary tumor growth and metastasis. Mol Cell Biol. 2009;29:3319–31.

      (4) Turpin J, Ling C, Crosby EJ, Hartman ZC, Simond AM, Chodosh LA, et al. The ErbB2DeltaEx16 splice variant is a major oncogenic driver in breast cancer that promotes a pro-metastatic tumor microenvironment. Oncogene. 2016;35:6053–64.

      (5) Ginzel JD, Acharya CR, Lubkov V, Mori H, Boone PG, Rochelle LK, et al. HER2 Isoforms Uniquely Program Intratumor Heterogeneity and Predetermine Breast Cancer Trajectories During the Occult Tumorigenic Phase. Mol Cancer Res. 2021; 19(10):1699–1711.

      (6) Ginzel JD, Chapman H, Sills JE, Allen EJ, Barak LS, Cardiff RD, et al. Nonlinear progression during the occult transition establishes cancer lethality. Dis Model Mech. 2025;18:dmm052113.

      (7) Kwong KY, Hung M-C. A novel splice variant of HER2 with increased transformation activity. Molecular Carcinogenesis. 1998;23:62–8.

      (8) Mitra D, Brumlik M, Okamgba S, Zhu Y, Duplessis T, Parvani J, et al. An oncogenic isoform of HER2 associated with locally disseminated breast cancer and trastuzumab resistance. Mol Cancer Ther. 2009;8(8):2152-62.

      (9) Castagnoli L, Ladomery M, Tagliabue E, Pupa SM. The d16HER2 Splice Variant: A Friend or Foe of HER2-Positive Cancers? Cancers (Basel). 2019;11.

      (10) Sperinde J, Jin X, Banerjee J, Penuel E, Saha A, Diedrich G, et al. Quantitation of p95HER2 in paraffin sections by using a p95-specific antibody and correlation with outcome in a cohort of trastuzumab-treated breast cancer patients. Clin Cancer Res. 2010;16:4226–35.

      (11) Dankort DL, Wang Z, Blackmore V, Moran MF, Muller WJ. Distinct tyrosine autophosphorylation sites negatively and positively modulate neu-mediated transformation. Mol Cell Biol. 1997;17:5410–25.

      (12) Dankort D, Maslikowski B, Warner N, Kanno N, Kim H, Wang Z, et al. Grb2 and Shc adapter proteins play distinct roles in Neu (ErbB-2)-induced mammary tumorigenesis: implications for human breast cancer. Mol Cell Biol. 2001;21:1540–51.

    1. eLife Assessment

      This is a valuable study that aims to resolve the mechanistic details of the final steps of bacterial lipoprotein trafficking. The evidence supporting the conclusions is mostly solid but support for other claims is incomplete. This work will be of interest to biochemists and microbiologists studying the bacterial cell envelope and those aiming to develop novel antibacterial molecules.

    2. Reviewer #1 (Public review):

      Summary:

      The manuscript aims to mechanistically characterize the final step of the Lol lipoprotein trafficking pathway in Gram-negative bacteria, where triacylated lipoproteins are transferred from the periplasmic chaperone protein LolA to the outer membrane receptor LolB for subsequent insertion into the inner leaflet of the outer membrane. The crystal structures of LolA R43L-LolB, LolB L114G-lipoprotein, LolB Q44A (open), and LolB Q44A (closed) are reported. A combination of structural analysis, growth assays, pull-downs, and fluorescence spectroscopy was employed to characterize the behaviour of wild-type and mutant forms of Lol proteins. The authors then used previously published molecular dynamics simulations to propose a six-step model for the transfer and insertion of lipoproteins from LolA to LolB, and finally into the membrane.

      The structural work is mostly solid, but the mechanistic conclusions drawn from it rely too much on interpolation between static states and on functional assays with limited dynamic range.

      Strengths:

      (1) The structure-guided mutagenesis was thorough and well designed. Mutants were tested both in isolation and in combination with one another, utilizing both alanine and charge reversal mutants, to fully tease apart which residues were most important for the interaction.

      (2) The dissection of the Hook interactions allowed the authors to identify the importance of the identity of the hook apex residue, as well as the critical hydrogen bond that maintained the Hook geometry.

      (3) The structure of the lipoprotein-bound LolB is a valuable addition to the body of knowledge around this pathway, and the way that the complex was produced was very clever.

      Weaknesses:

      (1) There is no raw data shown for any of the interactions or lipoprotein transfer experiments. All of the quantitative claims about LolA association and lipoprotein transfer rely on SDS-PAGE densitometry, and not a single gel image from those experiments is provided in the main text or supplemental.

      (2) Pull-down assays are a fairly poor assay to use for protein interactions when there are so many quantitative alternatives that would be amenable to this experimental system, especially when the association and transfer of lipoprotein are the central claims of the manuscript.

      (3) The crystal structures are under-refined for their resolution, with large R-factor gaps and somewhat concerning statistics contained in the wwPDB reports (e.g. >20% RSRZ outliers in 2/4 structures). The data also seem to be cut at strangely high I/sigI for the two Q44A structures with no explanation.

      (4) In the LolB L114G structure, the Hook residue L68 is the most poorly fit in the whole structure. The validation report indicates the entire hook is poorly resolved, but without access to the maps and models, we are unable to assess this independently.

      (5) There is no rubric for what constitutes a significant result for the DAUDA fluorescence spectroscopy. What level of increase vs wild-type is considered significant? I188G is incredibly close to L144G, but one is treated as a non-result and the other as something that should be further investigated.

      (6) The conformational trajectory of the proposed model is on static endpoints.

    3. Reviewer #2 (Public review):

      Summary:

      The manuscript by Jepson and colleagues presents the structural and biochemical study of the outer membrane LolB in complex with LolA in its apo and lipoprotein-bound states. The Lol system (LolABCDE) is essential in the translocation of lipoproteins to the outer membrane. Several studies have looked at the inner membrane complex. This study captured the last step of the process, the translocation of lipoproteins to the outer membrane via the LolB and LolA proteins. The crystal structures of LolB in complex with LolA revealed key interactions via the hook and charged belt motifs. Mutagenesis was not able to fully dissociate the complex or prevent the transfer of lipoprotein. The complex of LolB with LolA and lipoprotein revealed the 'final' step of engaging the complex for lipoprotein insertion into the outer membrane.

      Overall, this is an excellent and exciting study for the Lol field as it captures the events happening at the outer membrane.

      Strengths:

      The authors have determined several high-resolution structures and validated them with excellent mutagenesis experiments. The key structure is that of LolB with LolA and the lipoprotein (even partially resolved).

      Weaknesses:

      The only 'weakness' is that the authors have used mutants of either LolB or LolA to capture the stable states, and the discussion section would benefit from commenting if these states could be dead-end states, as validation of the mutants has been done with a WT background. In addition, can they discuss whether a2 is likely to undergo further conformational changes to open a lateral gate to the OM for the acyl chains of the lipoprotein to insert?

    1. eLife Assessment

      This valuable study carefully assesses the anatomical organization of the adult zebrafish olfactory bulb using a densely reconstructed electron microscopy dataset. The data were generated using a previously acquired state-of-the-art serial block-face scanning electron microscopy dataset, followed by manual classification of cells into distinct anatomical groups. The analysis is a convincing application of careful expert neuroanatomical understanding that reveals key details of the cell class and connectivity in the olfactory bulb, with helpful detailed descriptions of why each cell class was distinguished.

    2. Reviewer #1 (Public review):

      Summary

      The authors set out to address a critical gap in olfactory bulb research: the lack of a comprehensive annotation of neuron types and their connectivity in the adult zebrafish. Using a large serial block-face scanning electron microscopy volume, they reconstructed 459 neurons and performed a detailed morphological and ultrastructural classification. They identified 13 major neuron subclasses, including two projection neuron types (mitral cells and ruffed cells) and 11 interneuron types. A key finding is that despite the diffuse appearance of the lateral olfactory bulb neuropil, it is organized into discrete, segregated glomeruli. The authors also performed targeted synapse annotation, revealing systematic and selective connectivity patterns between projection neurons and specific interneuron subnetworks, and discovered distinct microcircuit motifs involving reciprocal and unidirectional connectivity. This work provides a crucial anatomical framework for understanding the circuit basis of olfactory computations.

      Strengths

      (1) The volumetric EM reconstructions capture fine ultrastructural features (e.g., spine shapes, neurite caliber variations, and varicosity morphology) at nanometer resolution across hundreds of neurons. The descriptive detail and 3D rendering provided for each subclass are remarkably thorough.

      (2) The study goes beyond simple morphological descriptions by integrating ultrastructural features, such as spine shape, neurite diameter, and synaptic arrangements, to define neuron classes. This provides a more functionally relevant taxonomy. The classification was also cross-validated by independent neuroscientists.

      (3) The targeted synapse annotation translates anatomy into testable circuit hypotheses, revealing distinct connectivity motifs (reciprocal vs. unidirectional, MC-selective vs. RC-selective) that provide structural substrates for computational functions such as normalization.

      Weaknesses

      (1) It is important to provide an estimate of the total neuronal population of the adult zebrafish OB and justify that their sample size is sufficient to claim a "comprehensive" neuron type classification.

      (2) The classification was based on morphology and ultrastructure. If feasible, we recommend an unsupervised clustering analysis using only the synaptic connectivity matrix for fully reconstructed neurons to test whether connectivity patterns recapitulate the 13 morphological subclasses. Besides, cross-validation by independent annotators is great. However, when the annotators disagreed on a neuron's subclass, how was it resolved?

      (3) A summarized reference table that includes features for each subclass is highly recommended (the main text cited several tables; however, I did not find them). This would facilitate community adoption of the classification.

      (4) The schematic in Figure 20 could be optimized to reflect synaptic connections among 13 subclasses with connection strength annotated.

    3. Reviewer #2 (Public review):

      Summary:

      The authors have used a previously published SBEM dataset of the adult zebrafish olfactory bulb and carefully reconstructed the fine structure of a large number (459) of neurons. Through manual classification of cells into 13 different anatomical classes, they provide a carefully annotated library of cells. Classifications have been validated by comparing the labeling of 5 human annotators, which revealed a large degree of reproducibility. Synaptic identification enables assignment of connectivity, revealing a major contribution of reciprocal connections (two-way synapses) that may support key computational features during olfactory processing. In general, the work provides a clean descriptive account of the organization of the zebrafish olfactory bulb, which should help to better understand principles of olfactory processing in vertebrates.

      Strengths:

      The authors provide a library of neurons in the olfactory bulb of adult zebrafish that will be highly useful to the broader circuit neuroscience community. Figures of classified cell types have been convincingly arranged to appreciate the high quality of the EM volume and reconstructions. Cells are easily accessible through a web interface to facilitate the assessment of classifications. An important result is that anatomical structures in zebrafish clearly follow the glomerular arrangement known in mammals, which argues against a microglomerulus model previously proposed for the zebrafish olfactory bulb.

      Weaknesses:

      The paper uses Wanner et al. 2016 as a main base dataset to generate a descriptive library of cells, in a limited volume (18%) of the zebrafish olfactory bulb. Annotations of cell types and synapses are largely human-based. Using a multi-human validation strategy helps to assess labeling, but it would benefit from more rigorous statistics, in particular for the synapse annotations. In general, the paper would be easier to read with a glossary of cell types. Some features are quite intriguing, like the cilia structures in some of the somata or the ruffs, but discussion of the potential computational roles of these fine structures is missing.

    4. Reviewer #3 (Public review):

      Summary:

      The authors aimed to build a comprehensive understanding of the cell classes that make up the adult zebrafish olfactory bulb (OB) and their connectivity. Using an EM volume covering the three OB layers and several glomeruli, they aim to reconstruct a sufficient sample of all cell classes in the OB. Based on 459 proofread reconstructions, they identify 13 cell classes and a small number of subclasses and offer detailed expert neuroanatomical arguments, largely qualitative, for why these classes are distinct. While they do not have a synapse classification, they further build a manual wiring diagram of connectivity based on these reconstructions, which together offer a strong baseline understanding of the organization of the OB and useful properties to build into a future quantitative assessment of fish neuroanatomy in general.

      Strengths:

      The paper offers a powerful example of why expert assessment remains a vital tool in neuroanatomy. While quantification is deeply important to scale data, the kind of careful multimodal human investigation of neuronal shape, connectivity, and ultrastructure forms a key basis for discovering and differentiating neuronal cell types. The authors use multi-expert classification to validate qualitative assessments as well, which is a good way to handle cross-individual uncertainty. The descriptions of each type and their relationships are generally quite thorough, and there are enough cells to offer a satisfying baseline categorization. Anyone studying zebrafish OB is likely to get a lot out of this paper.

      Weaknesses:

      While I am a strong believer in expert neuroanatomical intuition alongside quantitative neuroanatomical approaches to cell typing (especially given the manageable number of cells and cell types), I would have liked a section explaining why this approach was taken as opposed to a more quantitative one from the beginning. I think that this is a case study in where the expert qualitative approaches are useful, especially with fascinating details like the unusual hand-shaped spines. A lot of the strength of the paper comes from this philosophy, in my view, and it would be nice to hear it elucidated by the authors. I would also be curious if the authors came to any conclusions about what might have been possible with a more data-driven approach, although this may be beyond the scope of the work or not terribly interesting.

      There were some aspects of the cell typing that I didn't totally understand. Two of the most common issues with cell typing are that (1) it is unclear if cell types are discrete and (2) unusual cells appear that don't have enough peers to classify into an unambiguous type. Did those happen here? For example, it was not obvious to me how it was decided that mitral cells fell into three subclasses: small, medium, and large, and not a continuum from small to large. Similarly, some interneuron classes had subclasses. What anatomical qualities suggested that a split was a subclass-level split vs a class-level split? Were there any cells that were impossible to classify into a group, or was everything actually tidy? How confident are the authors that they have captured the complete diversity of the OB from this sampling?

      I generally appreciated the numerous detailed figures and found them generally clear and informative. However, as someone who is not deeply familiar with zebrafish olfactory bulb organization, I would have liked to have at least some global context for each cell class in terms of how typical cells relate to the layers and glomeruli. Figure 18B does a nice job of this with many cells, but it would be clearer to see this along the way with the individual classes (and subclasses) as well.

      For the connectivity analysis, I commend the authors for doing as much manual synapse annotation as they did, but the lack of completeness makes it less clear how to interpret the connectivity findings. In particular, how complete is the current assessment? For example, when describing connectivity between pairs of cells, how were the pairs selected? Did they necessarily have contact between the meshes, or innervate the same glomerulus, or were they just two cells of the same type in any location? This is useful to understand how to interpret the connectivity fractions measured.